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Paper Citation Record · LEDGER

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning

As of 9 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2507.03973.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.03973 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:10:59.966903Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

62 of 62 outbound references displayed

  • verified exact3
  • verified fuzzy50
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 993d0ab3-31b8-4909-adcb-ee3ecdc1e6cc · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Communication-efficient learning of deep networks from decentralized data,

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:54.533861Z digest=sha256:e78d882191732e4b95363cddf7b2a6b2b0d689c08b0e12879decc1c9603ea564

Observation c1c4aeaf-22de-43f5-b8c6-f6d32e380f06 · outbound

This paper cites Federated learning for the Internet of Things: Applications, challenges, and opportunities,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Federated learning for the Internet of Things: Applications, challenges, and opportunities,

Reference 2

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raw_fallback, observed 2026-08-06T20:11:03.448167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:54.595568Z digest=sha256:e71e0e7091aeb8fa5c8c8a5247ca9dade1a1a126b1c08aeed64eaf679fb6cd93

Observation 1e6ee25c-cae0-4341-aea9-097e416315ec · outbound

This paper cites Confederated learning: Federated learning with decentralized edge servers,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Confederated learning: Federated learning with decentralized edge servers,

Reference 3

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raw_fallback, observed 2026-08-06T20:11:03.436919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:54.692592Z digest=sha256:9261adf8d584b88d8fc9527cc956efdc4a87151b340d8940b340d2c7942107ef

Observation 59654741-e401-425a-92bc-0b53ec26cf30 · outbound

This paper cites A survey on federated learning,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning A survey on federated learning,

Reference 4

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raw_fallback, observed 2026-08-06T20:11:03.424847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:54.787154Z digest=sha256:ba4b2ff04f707e4da23265d72346013b6ae339d141a462163e797ce40c72cc0d

Observation be144260-4682-4f14-9eb7-22b7af74628f · outbound

This paper cites Heterogeneous feder- ated learning: State-of-the-art and research challenges,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Heterogeneous feder- ated learning: State-of-the-art and research challenges,

Reference 5

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raw_fallback, observed 2026-08-06T20:11:03.413512Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:54.878740Z digest=sha256:806bee3f443578ef4a066d4edb303de16401898c41345827b7287dba47236c64

Observation 6f99b6ca-d81c-487f-93f5-7a0f9f3f7bfb · outbound

This paper cites FedPD: A federated learning framework with adaptivity to Non-IID data,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning FedPD: A federated learning framework with adaptivity to Non-IID data,

Reference 6

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raw_fallback, observed 2026-08-06T20:11:03.399154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:54.949577Z digest=sha256:e7a0e989d2965f3a079dcbfe1cde1e8af598ff2d760173c29b84956e0ff3468a

Observation 09a99ebc-465a-43cd-ad98-ac2553936df4 · outbound

This paper cites Towards personalized federated learning,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Towards personalized federated learning,

Reference 7

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:55.021949Z digest=sha256:9166180b0a313d3b76931d6e1d279ad05e385cf65555804c4cb4c6d75a048ef6

Observation 26ed460c-8cf2-470a-9d23-ba754f4d6a96 · outbound

This paper cites Byzantine-robust and communication-efficient personalized federated learning,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Byzantine-robust and communication-efficient personalized federated learning,

Reference 8

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source=pdf_text observed=2026-08-06T20:10:55.088839Z digest=sha256:da7149818e1af8ee0c693b2746d44c4b94af4bbc85f1cc3f90c2493d8a80044d

Observation 4819e27b-9065-448e-9009-d2f525cd02c4 · outbound

This paper cites Adaptive model pruning and personalization for federated learning over wireless networks,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Adaptive model pruning and personalization for federated learning over wireless networks,

Reference 9

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raw_fallback, observed 2026-08-06T20:11:03.353527Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:55.169911Z digest=sha256:3f76d96602bb678b95856ff0a09b3942e1b38340b458f266bfc73505ff8b8411

Observation 4b6fefa9-01b3-4b69-b1a5-19af753314d4 · outbound

This paper cites Personalized federated learning towards communication efficiency, robustness and fairness,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Personalized federated learning towards communication efficiency, robustness and fairness,

Reference 10

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raw_fallback, observed 2026-08-06T20:11:03.341505Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:55.247820Z digest=sha256:ea016c188bc4861ef8e828eedf4fa880356bc1b65b5034f5753cd0df8754d3a7

Observation 377dd350-ed6f-4551-88e9-f996b0ef425c · outbound

This paper cites Communication-efficient design for quantized decentralized federated learning,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Communication-efficient design for quantized decentralized federated learning,

Reference 11

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:10:55.322851Z digest=sha256:5d97ec19975707f3aca633d7d82c84d7fecc53e2f3a1770ad308a94936dd08e3

Observation c776c5aa-152e-45ed-870a-f52a7f5832fa · outbound

This paper cites FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG

Reference 12

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local_arxiv, observed 2026-08-06T20:11:00.570927Z

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source=pdf_text observed=2026-08-06T20:10:55.368968Z digest=sha256:b7c2157dfffe6a22a48cd1481d0eba418bfc346d6b88b2ac2c5f99266f76aa40

Observation 474a43e3-41a0-4c28-8b83-99a0f3037f2d · outbound

This paper cites A survey of trustworthy federated learning: Issues, solutions, and challenges,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning A survey of trustworthy federated learning: Issues, solutions, and challenges,

Reference 13

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raw_fallback, observed 2026-08-06T20:11:03.320718Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:55.433398Z digest=sha256:9bca323cf63db638bb6bb5f21995edf298e247e30350e592b7f65c33ad089d6e

Observation 5cd67306-11e3-4dc8-afda-1e8b53be149b · outbound

This paper cites An experimental study of Byzantine- robust aggregation schemes in federated learning,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning An experimental study of Byzantine- robust aggregation schemes in federated learning,

Reference 14

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:55.503624Z digest=sha256:15c2fcbe1d1d05df561a9acb41ee8ff56ab878df4b4496c3a435a4af9cae54c1

Observation 141bd45f-cdb5-4718-a947-d0eef873210c · outbound

This paper cites A comprehensive survey of privacy- preserving federated learning: A taxonomy, review, and future direc- tions,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning A comprehensive survey of privacy- preserving federated learning: A taxonomy, review, and future direc- tions,

Reference 15

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:55.583144Z digest=sha256:46c8e5279f6a9951558179c833a8b58d0087c036846f2b8e75830b267707be0d

Observation 8f64aee9-5a1d-4303-8ef6-fa6a4dd68889 · outbound

This paper cites signSGD: Compressed optimisation for non-convex problems,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning signSGD: Compressed optimisation for non-convex problems,

Reference 16

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source=pdf_text observed=2026-08-06T20:10:55.662322Z digest=sha256:375b92a41e62219707f39446cb74098340a9846eb63248c8cd774bea8cabfe09

Observation 36a113d1-a4ec-448b-9ac2-eba98b3de409 · outbound

This paper cites signSGD with Majority Vote is Communication Efficient And Fault Tolerant.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning signSGD with Majority Vote is Communication Efficient And Fault Tolerant

Reference 17

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:10:55.711188Z digest=sha256:7440c51a7af03fa6be98c4b159937f0123e40421f5a0d5789a0bc262fc8f50c2

Observation 8e3a937e-a246-4927-8470-9066270d666e · outbound

This paper cites Distributed training with heterogeneous data: bridging median- and mean-based algorithms,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Distributed training with heterogeneous data: bridging median- and mean-based algorithms,

Reference 18

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:55.752847Z digest=sha256:976c0bc2287dd953f37d0bfc3f65a936c1deb5a4621e5550d3e1d941958f035a

Observation 15618c20-38fd-42bc-b163-8a8f410a6b19 · outbound

This paper cites Sign-based gradient descent with heterogeneous data: Convergence and Byzantine resilience,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Sign-based gradient descent with heterogeneous data: Convergence and Byzantine resilience,

Reference 19

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raw_fallback, observed 2026-08-06T20:11:03.262387Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:55.820749Z digest=sha256:784e4701b19b0ed19f46323eb01bf6b1ac306256105ba6176f69d79284518c9c

Observation 9835f748-50d8-4c25-9c2c-d1df812bd609 · outbound

This paper cites z-SignFedAvg: a unified stochastic sign-based compression for federated learning,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning z-SignFedAvg: a unified stochastic sign-based compression for federated learning,

Reference 20

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raw_fallback, observed 2026-08-06T20:11:03.251113Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:55.870023Z digest=sha256:7ee2a3c307fa2595363d164db7f5d040dc7791c41f163290950f572a12648644

Observation 721e3009-e5e0-478e-90fa-8678015c5e92 · outbound

This paper cites S 3GD-MV: Sparse-SignSGD with majority vote for communication-efficient distributed learning,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning S 3GD-MV: Sparse-SignSGD with majority vote for communication-efficient distributed learning,

Reference 21

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:55.943030Z digest=sha256:95410f29153a937ab6f4e078d7df0c636c95b36731d4211d0356b538f48f39b9

Observation b3b1607b-9981-4238-8d67-05ece76eea8b · outbound

This paper cites Federated optimization in heterogeneous networks,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Federated optimization in heterogeneous networks,

Reference 22

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:56.008461Z digest=sha256:0c8b619898a6ea94f10af34262b13cec5b93022338911967a87a2aa0c40d72b7

Observation 03eac1a6-31db-4051-a066-b3480d059c08 · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Federated Learning: Strategies for Improving Communication Efficiency

Reference 23

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no resolver link, observed 2026-08-06T20:10:56.075810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:10:56.075810Z digest=sha256:790aaf9a4dacf62da1498ad6e9e1af2225fb8843e45ac578a41e82218616fb50

Observation 66e55aca-81fe-4647-b28c-09b2f86d421e · outbound

This paper cites QSGD: Communication-efficient SGD via gradient quantization and encoding,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning QSGD: Communication-efficient SGD via gradient quantization and encoding,

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-06T20:11:03.219880Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:56.129715Z digest=sha256:d553e5fc76bb82939d39f799281230a7adff6ab139842c30ae15e6c0c43dc1fa

Observation cf2d074b-f0c1-4bc9-a16f-934f2d016a59 · outbound

This paper cites UVeQFed: Universal vector quantization for federated learning,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning UVeQFed: Universal vector quantization for federated learning,

Reference 25

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raw_fallback, observed 2026-08-06T20:11:03.209228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:56.170429Z digest=sha256:3ef10e89f981f7e77b71acee69a81f0b1654e522e3c7b32a6e405b57f3d81abf

Observation eedf6339-a524-4c49-b692-adaf0bd346ed · outbound

This paper cites Adaptive gradient quantization for data-parallel SGD,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Adaptive gradient quantization for data-parallel SGD,

Reference 26

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raw_fallback, observed 2026-08-06T20:11:03.199392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:56.252726Z digest=sha256:7559bb4dea5409187462b03f466ed08d4af27cd6d40671a2ef14faa4a5cea60c

Observation 3c1ed7b3-3958-473a-a5f8-d6cc4f36f877 · outbound

This paper cites Communication-efficient federated learning with adaptive quantiza- tion,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Communication-efficient federated learning with adaptive quantiza- tion,

Reference 27

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raw_fallback, observed 2026-08-06T20:11:03.188676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:56.341387Z digest=sha256:a16e15a19d5a49d2d3e6e3e3bc71ef62e361bc431bab35b0d4846107f47df9a7

Observation c178f8d0-8b2d-4c59-bb91-0b96b450a43e · outbound

This paper cites FedFQ: Federated Learning with Fine-Grained Quantization.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning FedFQ: Federated Learning with Fine-Grained Quantization

Reference 28

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verified exact
local_arxiv, observed 2026-08-06T20:11:00.389558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:56.393617Z digest=sha256:c8d58f8531fb53d1b1495c3470c48c7dfcf69b4d20e66aeffe16a99b1e576e7e

Observation dd41c9e3-0f0e-42fd-a548-ada942409148 · outbound

This paper cites Distributed deep reinforcement learning based gradient quantization for federated learning enabled vehicle edge computing,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Distributed deep reinforcement learning based gradient quantization for federated learning enabled vehicle edge computing,

Reference 29

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raw_fallback, observed 2026-08-06T20:11:03.177238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:56.485358Z digest=sha256:70af47e28209c9670e9d2fa64f55397fed1601e58b88a55a487fed6917f7e02c

Observation 2969dbb4-26e3-4d7b-ba02-841de7ecff2e · outbound

This paper cites Joint accuracy and latency optimization for quantized federated learning in vehicular networks,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Joint accuracy and latency optimization for quantized federated learning in vehicular networks,

Reference 30

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raw_fallback, observed 2026-08-06T20:11:03.164061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:56.573116Z digest=sha256:806969e670f665c6a8523f5e0338099d233f5720807a39f70ae253997003d819

Observation dbdf19c3-7cf0-4cd8-9de1-f3c753f630d6 · outbound

This paper cites The algorithmic foundations of differential privacy,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning The algorithmic foundations of differential privacy,

Reference 31

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raw_fallback, observed 2026-08-06T20:11:03.152368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:56.656474Z digest=sha256:3ce13b32441f9da92848305810bb9a8e45dd8d766476dbe57bf7004574ea5898

Observation 3f23b934-92c2-474d-a608-5a995191eade · outbound

This paper cites A survey on security and privacy of federated learning,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning A survey on security and privacy of federated learning,

Reference 32

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raw_fallback, observed 2026-08-06T20:11:03.138838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:56.770901Z digest=sha256:40dfed75ea888ae325a0c4d5f2114f294e251fc3dbf1233e21c402db34bb8774

Observation 7d0775e9-3344-4ff9-908a-bf159ae6e391 · outbound

This paper cites Differentially Private Federated Learning: A Client Level Perspective.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Differentially Private Federated Learning: A Client Level Perspective

Reference 33

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no resolver link, observed 2026-08-06T20:10:56.871054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:10:56.871054Z digest=sha256:fef301626a2f46c12daa173ede9f689b7a5a0006057a3a9773b30e2ef2f8f572

Observation 2fcd9125-ea0e-4bce-99d2-5decc0d7ee5b · outbound

This paper cites cpSGD: communication-efficient and differentially-private distributed SGD,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning cpSGD: communication-efficient and differentially-private distributed SGD,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:03.127670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:56.974900Z digest=sha256:31c9bf66246df4be81b1897e1b001260ed31619cacca4bd3ff48bcc333204fd1

Observation 584252c9-5e26-49be-8edb-5be0288a20f4 · outbound

This paper cites The Skellam mechanism for differentially private federated learning,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning The Skellam mechanism for differentially private federated learning,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:03.116049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:57.058426Z digest=sha256:746716b1a9f5a3a68ce19152953ebfef0090d7eb581534d5f6dacfaa41741b81

Observation 89da891c-ee5f-4be9-ad1e-ef8c72f02ac1 · outbound

This paper cites The distributed discrete Gaussian mechanism for federated learning with secure aggregation,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning The distributed discrete Gaussian mechanism for federated learning with secure aggregation,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:03.103513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:57.110423Z digest=sha256:8d8d0240634a4c21018f503b58d3b82de3387c538e53d7e7c63e978d86b3e30a

Observation e0ab9fc4-0ff2-4054-9768-2f1054222e22 · outbound

This paper cites Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T20:10:57.232378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:10:57.232378Z digest=sha256:3ac68f64ca087c14e8190f60fa4c55aee6fac5ede4dc14c9ed7e00ffd54300b9

Observation f9009b94-b4e4-4548-b4f5-b974ad5ed36a · outbound

This paper cites Joint privacy en- hancement and quantization in federated learning,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Joint privacy en- hancement and quantization in federated learning,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:03.091411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:57.298586Z digest=sha256:821e2638ca203654882bccd9335d26da342331ca530c64a39b1ff91b058bfc91

Observation c68631fb-2acb-4b8d-9d42-750d7da28d27 · outbound

This paper cites Randomized Quantization is All You Need for Differential Privacy in Federated Learning.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Randomized Quantization is All You Need for Differential Privacy in Federated Learning

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T20:10:57.412445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:10:57.412445Z digest=sha256:4529f49293a05557f661267fd07985178ba1d0f466a8cc2ab80f265000ecff7a

Observation d7b538e7-40fb-4019-80c1-73f8638f6ab2 · outbound

This paper cites vqSGD: Vector quantized stochastic gradient descent,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning vqSGD: Vector quantized stochastic gradient descent,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:03.075851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:57.530261Z digest=sha256:0a2e58095c92202be7efaa987d8eb241f15a4f08b4496f226f69b14dcc79605c

Observation 1644fa9c-582f-4384-bed4-eb7a15257e1f · outbound

This paper cites Machine learning with adversaries: Byzantine tolerant gradient descent,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Machine learning with adversaries: Byzantine tolerant gradient descent,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:03.062390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:57.637789Z digest=sha256:6ccebfe0b036f1b18c15b878ad4307933325e5d67210844921d915589bd7c245

Observation aed3f6a9-c627-446b-a196-33919dc51759 · outbound

This paper cites The hidden vulner- ability of distributed learning in Byzantium,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning The hidden vulner- ability of distributed learning in Byzantium,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:03.049993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:57.757035Z digest=sha256:c6dc6350ad61154eb36b8fb574e819985aee191cdeea7a2eb951688ea4073936

Observation dd049cb7-9c45-49e2-bf34-5a9a0e8122fd · outbound

This paper cites FABA: an algorithm for fast aggregation against Byzantine attacks in distributed neural networks,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning FABA: an algorithm for fast aggregation against Byzantine attacks in distributed neural networks,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:03.040191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:57.810904Z digest=sha256:426e323550866084923d811a2d3b78c6b6d9be51cb38459658c9eab7b3a6a8a2

Observation 2151e563-b5f5-4293-bf40-37429bd6bba6 · outbound

This paper cites Byzantine-robust dis- tributed learning: Towards optimal statistical rates,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Byzantine-robust dis- tributed learning: Towards optimal statistical rates,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:03.030304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:57.925576Z digest=sha256:24e4069ae83fcfdefdb6492444d71bcee71c024d8174e990a6d5b4e65c873c7f

Observation 1741777a-8b67-4a0f-9d55-46b1ef1b3e4d · outbound

This paper cites Robust aggregation for federated learning,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Robust aggregation for federated learning,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:03.019714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:58.009559Z digest=sha256:f671377043d6ec237d2c65362aa4fdfd80dc8acabfdd83c6672288b9a3aa36e7

Observation 3bf1e52e-eb09-4f63-a5f4-edb0e5e7cd4e · outbound

This paper cites DRACO: Byzantine-resilient distributed training via redundant gradients,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning DRACO: Byzantine-resilient distributed training via redundant gradients,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:03.009734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:58.121618Z digest=sha256:fcb671c94776ef368bed2e94507b83542292871bb794c50b4892cc8d6a648191

Observation 27017c96-fb38-4e7b-becc-94ae02052161 · outbound

This paper cites DETOX: a redundancy-based framework for faster and more robust gradient aggregation,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning DETOX: a redundancy-based framework for faster and more robust gradient aggregation,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:02.998201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:58.279625Z digest=sha256:a243edd966f0cf1f310fba01c657c2b686565cc43442efad13cde7e646c4edc3

Observation 47004a6c-e9cf-426c-ab4a-a6fc8283e435 · outbound

This paper cites Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-06T20:10:58.404804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:10:58.404804Z digest=sha256:d80d481f4c36a237b862ba7d736dccddec775e2bc04cd5b1f40cbe67db149c68

Observation 03f665a7-8fae-4030-b12f-c9f7cd59254c · outbound

This paper cites Byzantine-robust learning on heterogeneous data via gradient splitting,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Byzantine-robust learning on heterogeneous data via gradient splitting,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:02.973694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:58.499102Z digest=sha256:5b7698d8273e4a8dbdd6c52da96e262eb2e8f98baeab5d3afdcc015bd5060d8b

Observation 1be11789-7020-4fce-ae59-2a906597f22e · outbound

This paper cites Shielding federated learning: Robust aggregation with adaptive client selection,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Shielding federated learning: Robust aggregation with adaptive client selection,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:02.857707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:58.599805Z digest=sha256:359b1fcfff1da2a290fa070810cf713ab81039c34c8a6c147ca8ef4548b5932c

Observation f28693af-3414-49bd-b57a-64526f69acc0 · outbound

This paper cites Learning from history for Byzantine robust optimization,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Learning from history for Byzantine robust optimization,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:02.606928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:58.689872Z digest=sha256:53c1dcde9668d5299487671d7de9e24d1e9f9325766d82750ce622a02caab575

Observation 6eca968e-da75-4a7c-ada7-91f14968c5cb · outbound

This paper cites RSA: Byzantine- robust stochastic aggregation methods for distributed learning from heterogeneous datasets,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning RSA: Byzantine- robust stochastic aggregation methods for distributed learning from heterogeneous datasets,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:02.277415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:58.830671Z digest=sha256:8c6e8a5ba4827046adc6c69321e7da3ccd143332503c0c33ca8e804e42857327

Observation b6d6fa56-ff21-4123-8966-abae3e3211ff · outbound

This paper cites Federated Two-stage Learning with Sign-based Voting.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Federated Two-stage Learning with Sign-based Voting

Reference 53

Resolution
verified exact
local_arxiv, observed 2026-08-06T20:11:00.151557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:58.917613Z digest=sha256:2daa19ad1af21cab1552024fe926a2b817ca416da5fc1f8d454a4832eda5174b

Observation ded8ca80-560c-4d0f-aa92-45a267b3dcb7 · outbound

This paper cites Stochastic sign descent methods: New algorithms and better theory,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Stochastic sign descent methods: New algorithms and better theory,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:02.047980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:59.046534Z digest=sha256:b523a0647d4df054a7dcda6919d42cc47228a059a541dfd041627a62d48e9f1a

Observation 548a11e9-87af-45c0-b21d-8ec5d8af2ec5 · outbound

This paper cites Bridging differential privacy and Byzantine- robustness via model aggregation,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Bridging differential privacy and Byzantine- robustness via model aggregation,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:01.900715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:59.150706Z digest=sha256:df053ec5522222e929c9ef35852e3325e6c202c44e20967226f2e62dc62724bf

Observation be129ca0-9510-4990-a1f2-76a5ff5eedd2 · outbound

This paper cites Federated learning with ℓ1 regularization,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Federated learning with ℓ1 regularization,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:01.745093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:59.266514Z digest=sha256:d17a8377b5d3d26cd1200b27c29d5308c55065ae69d088b61b9fb911d8c4a9a1

Observation 4547000c-bd3b-4034-b740-f71d22e73986 · outbound

This paper cites Mag- nitude matters: Fixing signSGD through magnitude-aware sparsification and error feedback in the presence of data heterogeneity,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Mag- nitude matters: Fixing signSGD through magnitude-aware sparsification and error feedback in the presence of data heterogeneity,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:01.570083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:59.391233Z digest=sha256:c6cb51f680cf556ce48b401678551bafb7f569b51601602ba3b15043cb2a1ac3

Observation 06249ae1-99a1-479b-ad62-1cbada1d15bd · outbound

This paper cites Rate distortion for model compression:From theory to practice,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Rate distortion for model compression:From theory to practice,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:01.384890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:59.514306Z digest=sha256:7062fafadcbd5ae2be4df6b39af8a44ee59a213c19355d2dc23445ebb59a482b

Observation 46bf3896-3c3a-408a-9c70-8f1b220a81be · outbound

This paper cites Deep learning with differential privacy,.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Deep learning with differential privacy,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:01.205254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:59.622025Z digest=sha256:49e00cc803293b82e66521969ce81260e41b999ed06d6e206e918e215c7ed25d

Observation cc8423fa-00f9-4a14-9847-4f7140634fe8 · outbound

This paper cites an unresolved cited work.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Unresolved cited work

Reference 60

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:11:01.029203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:59.751241Z digest=sha256:b33df0bcab5bcd0a6521ae76a8198a1c06dd3f4265484864ffef5a89bb3b00ea

Observation cf401d66-ee1a-4ec3-881f-34574705555c · outbound

This paper cites an unresolved cited work.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning Unresolved cited work

Reference 61

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:11:00.884882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:59.853155Z digest=sha256:fd25163fb9ce9896bead9002e8bfb55870a12a67b2b92284379954ec142a3d06

Observation 5a9041d9-175a-4bd7-8f56-d6649638e360 · outbound

This paper cites 1 M 2 MX m=1 I {cm i = 1} + X i∈B I {zm i > cm i } − X i∈B I {zm i < cm i } ! − M ! bi #2 − θi 2 = E.

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning 1 M 2 MX m=1 I {cm i = 1} + X i∈B I {zm i > cm i } − X i∈B I {zm i < cm i } ! − M ! bi #2 − θi 2 = E

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:11:00.724217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:10:59.966903Z digest=sha256:f7f64d62dd9a51a80410865579fd1fe10069cfb179fdba6a4575afe45edcb50a

Pith citing papers

No inbound Pith citation observations are available.