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Paper Citation Record · LEDGER

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures

As of 20 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2505.06497.

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

pith.paper-citation-record.v1
2505.06497 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:45:38.943461Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

32 of 32 outbound references displayed

  • verified exact4
  • verified fuzzy15
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 37cb8087-9bdd-4bb8-8583-1c133404b56c · outbound

This paper cites Federated learning: Challenges, methods, and future directions.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Federated learning: Challenges, methods, and future directions

Reference 1

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Observation 994f4c8e-a787-4d26-bf1a-c2c6a863af44 · outbound

This paper cites Adaptive federated learning in resource constrained edge computing systems.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Adaptive federated learning in resource constrained edge computing systems

Reference 2

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raw_fallback, observed 2026-08-15T22:45:39.437028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation aca7e0fe-5d19-4e76-95e4-2356f618abf6 · outbound

This paper cites Personalized federated learning with theoretical guarantees: A model-agnostic meta- learning approach.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Personalized federated learning with theoretical guarantees: A model-agnostic meta- learning approach

Reference 3

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 96c4fe71-a152-4f3b-a632-452c0639c0c6 · outbound

This paper cites When deep reinforcement learning meets federated learning: Intelligent multitimescale resource management for multiaccess edge computing in 5g ultradense network.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures When deep reinforcement learning meets federated learning: Intelligent multitimescale resource management for multiaccess edge computing in 5g ultradense network

Reference 4

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raw_fallback, observed 2026-08-15T22:45:39.406440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T22:45:38.817274Z digest=sha256:4151cdf0da1e1091b6fb3978cfca5c47559330179c969a796eaf5c5fcf689ff9

Observation 1c377d62-1f68-4ab2-91ef-0890f0bb5e5e · outbound

This paper cites HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

Reference 5

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Observation 778e89e7-21a4-4e9d-929b-305325f04e89 · outbound

This paper cites Efficient federated learning on resource-constrained edge devices based on model pruning.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Efficient federated learning on resource-constrained edge devices based on model pruning

Reference 6

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raw_fallback, observed 2026-08-15T22:45:39.391394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 17f0761b-7ddf-492a-9e6f-318d4dc07605 · outbound

This paper cites Federated learning for resource- constrained iot devices: Panoramas and state of the art.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Federated learning for resource- constrained iot devices: Panoramas and state of the art

Reference 7

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T22:45:38.832148Z digest=sha256:638c34fb22f1405dcacec0f33cae86a9e9dfd517c69c9bf24e3d7c2173d939ec

Observation 5477de2b-16a4-44f3-8e8b-ac7ae4102dab · outbound

This paper cites Federated multi-task learning.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Federated multi-task learning

Reference 8

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source=pdf_text observed=2026-08-15T22:45:38.836494Z digest=sha256:dc4161ff4a9f3baf5042457bc734d2f562618e8e633aa8e50bb982ed5a27ded7

Observation 26115598-af53-4906-82ec-cfef8c274150 · outbound

This paper cites Flexifed: Personalized federated learning for edge clients with heterogeneous model architectures.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Flexifed: Personalized federated learning for edge clients with heterogeneous model architectures

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T22:45:38.840785Z digest=sha256:15067d3d88d679da4557709c2def4c095b53c49481ea4b47a5e92261e8871012

Observation ca11f5b5-5e9f-4e71-b553-1e36023e3b75 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 10

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source=pdf_text observed=2026-08-15T22:45:38.844853Z digest=sha256:b5e8f54fb1653ec87444440f70a257bc3efac8ae0f4800f7cc52a14a01dc5de8

Observation 8a307e5a-f6be-482b-998d-91d3c1f5def4 · outbound

This paper cites Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints

Reference 11

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source=pdf_text observed=2026-08-15T22:45:38.849435Z digest=sha256:4ba8d7e76594a46c11180bc67652aa7f92c6e23c060243f78ac0fe66e396317e

Observation 2aaa5f79-48c8-45be-a230-95f400ee1581 · outbound

This paper cites Neural Architecture Search with Reinforcement Learning.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Neural Architecture Search with Reinforcement Learning

Reference 12

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source=pdf_text observed=2026-08-15T22:45:38.853670Z digest=sha256:3eec6041800eaeb48c025e3d6f6f0f28dc86868960f38aea960a919a9e21a90f

Observation b59e5ffd-dadc-42a5-bc9e-5cd9892c249a · outbound

This paper cites SPIDER: Searching Personalized Neural Architecture for Federated Learning.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures SPIDER: Searching Personalized Neural Architecture for Federated Learning

Reference 13

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local_arxiv, observed 2026-08-15T22:45:39.116575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T22:45:38.858058Z digest=sha256:11fb49bbbfacc35a908e29a5dc91c6cfd4b2dd082d731b326cc9264f67575ef0

Observation 81da4e48-f501-4f0f-97b9-b410b2e193a2 · outbound

This paper cites Personalized federated learning via heterogeneous modular networks.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Personalized federated learning via heterogeneous modular networks

Reference 14

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raw_fallback, observed 2026-08-15T22:45:39.326808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T22:45:38.862946Z digest=sha256:d505620a16742949ae88a8daad6ac94fa1b4d99df154f64c81a6f1438b984fd9

Observation 6c29186a-dac7-4650-b869-ac1f5ddea1b5 · outbound

This paper cites Ditto: Fair and robust federated learning through personalization.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Ditto: Fair and robust federated learning through personalization

Reference 15

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raw_fallback, observed 2026-08-15T22:45:39.311554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T22:45:38.867210Z digest=sha256:e7bfa4e9f0349fb02e83cdc98ae49f38d98be2d734ad8dddbca1327167cc0c3a

Observation b286ef1f-6a3d-4db7-a8c2-c22a4883c82b · outbound

This paper cites FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts

Reference 16

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source=pdf_text observed=2026-08-15T22:45:38.871603Z digest=sha256:aab2a82b9aad7863c6e1a5471b02fa15f3e4cf59d88e27becc1607d650dd4a13

Observation a98be706-9d03-4bd3-962a-9fd98465fcca · outbound

This paper cites Federated optimization in heterogeneous networks.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Federated optimization in heterogeneous networks

Reference 17

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Observation fc917a9c-f6c9-473e-9859-1b8c7fdc2d0a · outbound

This paper cites Federated learning on non-iid data silos: An experimental study.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Federated learning on non-iid data silos: An experimental study

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T22:45:38.880588Z digest=sha256:fc8b92622ff2c6a9fe5019d53b1624ec2f77ba0d373adff7deec099fd5548d6c

Observation 0fe740ea-280a-4230-aab5-a68048be32a6 · outbound

This paper cites Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approach.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approach

Reference 19

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local_arxiv, observed 2026-08-15T22:45:39.079872Z

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source=pdf_text observed=2026-08-15T22:45:38.885046Z digest=sha256:6095a8e2874f7598775575d69ec4a68e00900021514e458f628abd736805d890

Observation d32cb114-d647-4d0c-bc94-5a4f540c1e3a · outbound

This paper cites Fedada: Fast-convergent adaptive federated learning in heterogeneous mobile edge computing environment.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Fedada: Fast-convergent adaptive federated learning in heterogeneous mobile edge computing environment

Reference 20

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T22:45:38.889676Z digest=sha256:92486e4782d5958ebdcd2ae32639e206f03d420554d4b48c2b8b6b2618a8350d

Observation 8c98a0cb-1238-48f8-90b9-f1feca96ce33 · outbound

This paper cites Net2Net: Accelerating Learning via Knowledge Transfer.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Net2Net: Accelerating Learning via Knowledge Transfer

Reference 21

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source=pdf_text observed=2026-08-15T22:45:38.894021Z digest=sha256:08944f72f006c4ad0c04126428bd1a66ef02d19bbb56174981c97b298c772829

Observation 7ed9aea3-6d67-4f99-8f05-f473e8242fd3 · outbound

This paper cites {ModelKeeper}: Accelerating {DNN} training via automated training warmup.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures {ModelKeeper}: Accelerating {DNN} training via automated training warmup

Reference 22

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T22:45:38.898939Z digest=sha256:411930883284ab2fecaa2e80e728523085745a91dea0f8559913e887aca74868

Observation deb3a6c9-b875-4b57-ada2-236956b76baa · outbound

This paper cites Hetefedrec: Federated recommender systems with model heterogeneity.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Hetefedrec: Federated recommender systems with model heterogeneity

Reference 23

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raw_fallback, observed 2026-08-15T22:45:39.239376Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T22:45:38.903235Z digest=sha256:8361831e7677dc2c343dc8a50a579c076381187633b716afc2a82cde87eae8f7

Observation f0261e16-d74b-40f1-82f2-e0b8ef086004 · outbound

This paper cites Hdhrfl: A hierarchical robust federated learning framework for dual-heterogeneous and noisy clients.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Hdhrfl: A hierarchical robust federated learning framework for dual-heterogeneous and noisy clients

Reference 24

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T22:45:38.907538Z digest=sha256:4c10b0c9783b00c1272e7c0d6053ac8ae75860d49023d514ada6093d0c02b0e7

Observation e55102ef-4eda-4dc6-8836-a84fef3f160a · outbound

This paper cites AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems

Reference 25

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local_arxiv, observed 2026-08-15T22:45:39.041101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T22:45:38.912020Z digest=sha256:51a35f92064ce4d004859518ee4375cfd56ac1913063839922acf43263ac9285

Observation 07d8a41c-8e3d-452b-95ef-9bfe155db3ad · outbound

This paper cites FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction

Reference 26

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source=pdf_text observed=2026-08-15T22:45:38.916593Z digest=sha256:2063bfd5ddcb9f766c8cbece5fd3efe82ca77e442cd79e82b62a5abb17f9e294

Observation 82102d3b-aae6-4129-a733-bfa18eab60c3 · outbound

This paper cites EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning

Reference 27

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local_arxiv, observed 2026-08-15T22:45:39.002529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T22:45:38.920942Z digest=sha256:40e7da2970cafc978a0ac0df4fcb29259b9c4d2f8b54f9567dd184993787e28e

Observation cbf61191-0496-45bb-aeb6-e2a64603541b · outbound

This paper cites A stochastic approximation method.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures A stochastic approximation method

Reference 28

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source=pdf_text observed=2026-08-15T22:45:38.925417Z digest=sha256:d5992143c28fd8989c947e5531c9dfc877dfd2cdc8966c758c0cbce03808584b

Observation 69269d3c-4b3c-4880-8ebf-48326ccdf270 · outbound

This paper cites Survey of personalization techniques for federated learning.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Survey of personalization techniques for federated learning

Reference 29

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raw_fallback, observed 2026-08-15T22:45:39.198389Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T22:45:38.929726Z digest=sha256:a03cf10070a354ef4b17f8507aea9ae43b591ca2215adc492de46406b7bbf422

Observation 6eefe464-a198-4510-8a7f-07ad5ed052a1 · outbound

This paper cites Gradient-based learning applied to document recognition.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Gradient-based learning applied to document recognition

Reference 30

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source=pdf_text observed=2026-08-15T22:45:38.934313Z digest=sha256:e729860352fa2f998bc48a882f4e4aabb46743a43ad42c2eac233bf217f6a0f3

Observation c4e29fb3-a90c-4703-8c99-3243e17bf973 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 31

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source=pdf_text observed=2026-08-15T22:45:38.938717Z digest=sha256:ae9e7131b511ef03b9814e7c04d3e3cd9d3bca0b6780b0e244ab4a93427a417d

Observation 516a6f33-e256-45b8-8553-e5e7328fc2aa · outbound

This paper cites Learning multiple layers of features from tiny images.

FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Learning multiple layers of features from tiny images

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-15T22:45:39.175402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Pith citing papers

No inbound Pith citation observations are available.