Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates | Paper Citation Record

Observation c781e0ab-169f-4ae3-ba2c-7813f2ecef6d · outbound

This paper cites Advances and open problems in federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Advances and open problems in federated learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:59.700565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b78b8e1b-d96f-4af8-b366-8e8f7a5cc5d0 · outbound

This paper cites Federated learning review: Fundamen- tals, enabling technologies, and future applications,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Federated learning review: Fundamen- tals, enabling technologies, and future applications,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:59.686620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 84a97f4b-bdf1-4651-a4a9-85d139246a10 · outbound

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

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates A survey of trustworthy federated learning: Issues, solutions, and challenges,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:59.672672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 607cf3b6-2e23-4111-ac94-64ef081bd124 · outbound

This paper cites Trustworthy federated learn- ing: Privacy, security, and beyond,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Trustworthy federated learn- ing: Privacy, security, and beyond,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:59.658056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation be9fbfa3-5dab-4a60-acae-67248d9ee715 · outbound

This paper cites Deep leakage from gra- dients,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Deep leakage from gra- dients,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:59.644749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3353c9c9-5495-4978-a5f3-d677f1cea5a3 · outbound

This paper cites iDLG: Improved Deep Leakage from Gradients.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates iDLG: Improved Deep Leakage from Gradients

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:46.220200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51c122de-d92a-462a-bd4b-91b456a6f93e · outbound

This paper cites See through gradients: Image batch recovery via gradinversion,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates See through gradients: Image batch recovery via gradinversion,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:59.630834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e412ea75-1239-4322-bb66-f918fc5c2650 · outbound

This paper cites SoK: On gradient leakage in federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates SoK: On gradient leakage in federated learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:59.614014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9b0efb19-7359-41d4-aa62-ed7f93434327 · outbound

This paper cites More than enough is too much: Adaptive defenses against gradient leakage in production federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates More than enough is too much: Adaptive defenses against gradient leakage in production federated learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:59.417330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1a15e86d-de4a-4b67-bb41-2aa3f6ec4bbf · outbound

This paper cites Fishing for user data in large-batch federated learning via gradient magnification,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Fishing for user data in large-batch federated learning via gradient magnification,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:59.127349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a54ac196-3d50-465e-b9b0-889214b8be4d · outbound

This paper cites When the curious abandon honesty: Federated learning is not private,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates When the curious abandon honesty: Federated learning is not private,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:58.916127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1c75a246-4870-4ca7-9248-5ef7caff7d08 · outbound

This paper cites Hiding in plain sight: Disguising data stealing attacks in federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Hiding in plain sight: Disguising data stealing attacks in federated learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:58.710174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e02eb0aa-13bd-4001-9733-0cc645df97ad · outbound

This paper cites Privacy and robustness in federated learning: Attacks and defenses,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Privacy and robustness in federated learning: Attacks and defenses,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:58.520896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 284af255-dc86-4e6f-a4fc-b9d34b1c3d88 · outbound

This paper cites Challenges and approaches for mitigating Byzantine attacks in federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Challenges and approaches for mitigating Byzantine attacks in federated learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:58.401964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fd1f8d7b-a77e-472b-a910-92e8c0fa1797 · outbound

This paper cites Back to the drawing board: A critical eval- uation of poisoning attacks on production federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Back to the drawing board: A critical eval- uation of poisoning attacks on production federated learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:58.269979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 752ffbc9-255d-41a1-aaa8-8cd1ef2a0a56 · outbound

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

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates An experimen- tal study of Byzantine-robust aggregation schemes in federated learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:58.164620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 085ffcc2-13f0-40f3-ae28-f103c6e82c1a · outbound

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

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Communication-efficient learn- ing of deep networks from decentralized data,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:57.985467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f27e2b4-e8d3-4e21-a665-f03e6fb288c2 · outbound

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

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Machine learning with adversaries: Byzantine tolerant gradient descent,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:57.791493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 63d5a2e5-40d6-41b4-b9dc-6a2917996890 · outbound

This paper cites Byzantine-robust distributed learning: Towards opti- mal statistical rates,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Byzantine-robust distributed learning: Towards opti- mal statistical rates,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:57.636583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bf11e134-3a0b-4ab2-815d-d9037c98c250 · outbound

This paper cites A systematic review of ho- momorphic encryption and its contributions in health- care industry,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates A systematic review of ho- momorphic encryption and its contributions in health- care industry,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:57.511718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f18eb849-2b01-427d-8850-6389e0809820 · outbound

This paper cites ELSA: Secure aggregation for federated learning with ma- licious actors,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates ELSA: Secure aggregation for federated learning with ma- licious actors,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:57.374666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2c3d4f34-409d-4473-abd4-da6acb3ba810 · outbound

This paper cites Gradient-leakage resilient federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Gradient-leakage resilient federated learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:57.305461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 46b97acd-95ca-46e4-aed3-fe5d3505a525 · outbound

This paper cites Does differential privacy really protect fed- erated learning from gradient leakage attacks?.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Does differential privacy really protect fed- erated learning from gradient leakage attacks?

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:57.182495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4b5271b5-8aa0-4057-b937-ebe6fde1f85f · outbound

This paper cites Manipulating the Byzantine: Optimizing model poisoning attacks and defenses for federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Manipulating the Byzantine: Optimizing model poisoning attacks and defenses for federated learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:57.032721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3b70270b-efaf-4b2d-ba52-d40479f1b845 · outbound

This paper cites FLTrust: Byzantine-robust federated learning via trust bootstrap- ping,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates FLTrust: Byzantine-robust federated learning via trust bootstrap- ping,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:56.869485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 85f19679-a210-4e66-9116-cbb2ff4398da · outbound

This paper cites Byzantine-robust decentralized federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Byzantine-robust decentralized federated learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:56.754476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bd75ad2e-ceb9-4b5f-a60b-3420b45e2475 · outbound

This paper cites On the privacy-robustness-utility trilemma in distributed learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates On the privacy-robustness-utility trilemma in distributed learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:56.614889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 70b508cd-c579-483f-b770-b77fc7e725b2 · outbound

This paper cites Fed- erated learning: Challenges, methods, and future di- rections,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Fed- erated learning: Challenges, methods, and future di- rections,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:56.503857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f0c43998-6ed1-4a63-b395-da2e5438ecf9 · outbound

This paper cites Inverting gradients – How easy is it to break privacy in federated learning?.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Inverting gradients – How easy is it to break privacy in federated learning?

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:56.333755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7d24b99c-b46f-49da-8c7e-137eefdb6f24 · outbound

This paper cites Truth serum: Poisoning machine learning models to reveal their secrets,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Truth serum: Poisoning machine learning models to reveal their secrets,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:56.140348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ff404445-1c0c-440d-9f1b-4f4474fcb050 · outbound

This paper cites Membership inference attacks against machine learn- ing models,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Membership inference attacks against machine learn- ing models,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:55.946927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e794b154-da16-447f-9163-5c2cc062b6c2 · outbound

This paper cites Membership inference attacks and defenses in fed- erated learning: A survey,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Membership inference attacks and defenses in fed- erated learning: A survey,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:55.641881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e9b2dda2-d206-4157-b754-86a2f334280c · outbound

This paper cites Compre- hensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Compre- hensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:55.371027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d83ba20c-bbb9-4af7-8540-ad9f87598fbe · outbound

This paper cites Exploiting unintended feature leakage in collaborative learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Exploiting unintended feature leakage in collaborative learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:55.162395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f34e244c-ee6e-48d7-aaf8-631f516aeca6 · outbound

This paper cites Deep models under the GAN: Information leakage from collabora- tive deep learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Deep models under the GAN: Information leakage from collabora- tive deep learning,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:54.889761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation af389030-0eb7-4e70-a2b4-313a140343c3 · outbound

This paper cites FedInverse: Evaluating privacy leak- age in federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates FedInverse: Evaluating privacy leak- age in federated learning,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:54.574162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 783fe47e-28dd-4a88-8cbb-85ff4b1deccb · outbound

This paper cites Rethinking privacy preserving deep learning: How to evaluate and thwart privacy attacks,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Rethinking privacy preserving deep learning: How to evaluate and thwart privacy attacks,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:54.296033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e4508eb4-cf9b-478e-9fa0-ddfc1dc4950b · outbound

This paper cites The resource problem of using linear layer leakage attack in federated learn- ing,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates The resource problem of using linear layer leakage attack in federated learn- ing,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:54.068145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8673d94a-7f52-4129-8de7-5f47b47002a5 · outbound

This paper cites FedSecurity: A benchmark for attacks and defenses in federated learning and federated LLMs,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates FedSecurity: A benchmark for attacks and defenses in federated learning and federated LLMs,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:53.873055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dc9b02cb-5942-491b-987b-d7d6d4ef0722 · outbound

This paper cites Fall of empires: Breaking byzantine-tolerant SGD by inner product ma- nipulation,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Fall of empires: Breaking byzantine-tolerant SGD by inner product ma- nipulation,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:53.712490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 68b29ff4-e92f-48b7-99ef-94bb50b752bf · outbound

This paper cites 3dFed: Adaptive and extensible framework for covert backdoor attack in federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates 3dFed: Adaptive and extensible framework for covert backdoor attack in federated learning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:53.510009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fe756282-dd90-47d1-805b-a8cc386b1905 · outbound

This paper cites Data and model poisoning backdoor attacks on wireless federated learning, and the defense mecha- nisms: A comprehensive survey,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Data and model poisoning backdoor attacks on wireless federated learning, and the defense mecha- nisms: A comprehensive survey,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:53.349711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 63cdd97b-d8c8-4e0b-8ff2-168a2d85bf4e · outbound

This paper cites Byzantines can also learn from history: Fall of cen- tered clipping in federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Byzantines can also learn from history: Fall of cen- tered clipping in federated learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:53.231705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c8af39fc-5e88-4d80-868b-685e87548599 · outbound

This paper cites AgrEvader: Poisoning membership inference against Byzantine-robust federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates AgrEvader: Poisoning membership inference against Byzantine-robust federated learning,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:53.019840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6bad38e7-853a-4f1b-9c8c-9b7d4ba24f26 · outbound

This paper cites Loden: Making every client in federated learning a defender against the poisoning membership inference attacks,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Loden: Making every client in federated learning a defender against the poisoning membership inference attacks,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:52.906415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 270bd0fb-5d38-4f40-8bd6-9e40f45efb15 · outbound

This paper cites Understanding clipping for federated learning: Con- vergence and client-level differential privacy,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Understanding clipping for federated learning: Con- vergence and client-level differential privacy,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:52.731694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d6b09afb-fb39-4331-b041-59bf89352088 · outbound

This paper cites Deep learning with differential privacy,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Deep learning with differential privacy,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:52.580245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ad9adfc5-4626-47be-a859-c5acde89d021 · outbound

This paper cites User-level privacy-preserving feder- ated learning: Analysis and performance optimization,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates User-level privacy-preserving feder- ated learning: Analysis and performance optimization,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:52.394575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c24e8e41-b75f-48f2-89cf-8f40f671126e · outbound

This paper cites Federated latent dirich- let allocation: A local differential privacy based frame- work,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Federated latent dirich- let allocation: A local differential privacy based frame- work,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:52.241157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c28f3676-59e5-416e-afa0-93029f912b41 · outbound

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

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates The distributed discrete gaussian mechanism for federated learning with secure aggregation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:52.075749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dbf17113-00eb-44e1-8a7b-f0261c9bc633 · outbound

This paper cites Differentially-private fed- erated linear bandits,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Differentially-private fed- erated linear bandits,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:51.895453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c93fae99-7d77-44b2-9e2f-d2a733cde1dd · outbound

This paper cites Practical secure aggregation for federated learning on user-held data,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Practical secure aggregation for federated learning on user-held data,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:51.716643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0f33741c-6ad4-41e5-ba22-e32556731233 · outbound

This paper cites A survey on federated learning: A perspective from multi-party computation,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates A survey on federated learning: A perspective from multi-party computation,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:51.561816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3b6490e7-d6ea-4bb2-8eba-33b72839b2f0 · outbound

This paper cites Secure federated learning with fully ho- momorphic encryption for iot communications,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Secure federated learning with fully ho- momorphic encryption for iot communications,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:51.374675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dd62adbc-d395-4977-9a14-341d3fdee0ad · outbound

This paper cites Blockchain-based federated learning with secure aggregation in trusted execution environment for internet-of-things,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Blockchain-based federated learning with secure aggregation in trusted execution environment for internet-of-things,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:51.178537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c891c439-1cff-4368-9fb9-35c96f2d577f · outbound

This paper cites MESAS: Poisoning defense for federated learning resilient against adaptive attackers,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates MESAS: Poisoning defense for federated learning resilient against adaptive attackers,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:51.004232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7f3ecbdf-7456-4164-ac32-06c5c42d5e55 · outbound

This paper cites FreqFed: A frequency analysis- based approach for mitigating poisoning attacks in federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates FreqFed: A frequency analysis- based approach for mitigating poisoning attacks in federated learning,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:50.861791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f02387a9-a645-4e3c-949d-25e1843c30a3 · outbound

This paper cites Byzantine- robust federated learning through collaborative mali- cious gradient filtering,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Byzantine- robust federated learning through collaborative mali- cious gradient filtering,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:50.660257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6bb96b86-690b-4298-bb17-fdfb54239f4c · outbound

This paper cites Differential privacy,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Differential privacy,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:50.469933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a5a6a13c-e126-4435-a0c9-5399949fd516 · outbound

This paper cites Differential privacy meets federated learning under communication constraints,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Differential privacy meets federated learning under communication constraints,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:50.272327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4b44f833-0ea3-4cbd-bb34-d7e75bbbf11b · outbound

This paper cites Local SGD converges fast and communi- cates little,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Local SGD converges fast and communi- cates little,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:50.089467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f98315f-4661-4513-99a3-131898c5d54f · outbound

This paper cites FL-NTK: A neural tangent kernel-based framework for federated learning analysis,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates FL-NTK: A neural tangent kernel-based framework for federated learning analysis,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:49.969532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b5784925-56b1-444f-807e-648a9ec331c0 · outbound

This paper cites Recon- structing individual data points in federated learning hardened with differential privacy and secure aggrega- tion,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Recon- structing individual data points in federated learning hardened with differential privacy and secure aggrega- tion,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:49.810769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e85eb1b0-2660-456f-97a9-3d73b7e41400 · outbound

This paper cites Understanding gradient clipping in private SGD: A geometric per- spective,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Understanding gradient clipping in private SGD: A geometric per- spective,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:49.593683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 09c13e6e-01d7-4f81-ba16-b647fc9bfe1c · outbound

This paper cites Autoencoders, unsupervised learning, and deep architectures,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Autoencoders, unsupervised learning, and deep architectures,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:49.376011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e8d49b20-0fe1-4d7c-8e8e-23249c052538 · outbound

This paper cites Robustness of graph neural networks at scale,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Robustness of graph neural networks at scale,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:49.165165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation df9fe45b-f9cc-44ba-9169-58bb36d53232 · outbound

This paper cites RSA: Byzantine-robust stochastic aggrega- tion methods for distributed learning from heteroge- neous datasets,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates RSA: Byzantine-robust stochastic aggrega- tion methods for distributed learning from heteroge- neous datasets,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:48.913107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c36e54a2-6790-40e7-b707-59d8b38f9636 · outbound

This paper cites Neurotoxin: Durable backdoors in federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Neurotoxin: Durable backdoors in federated learning,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:48.759225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9f4b5d07-f640-4049-87fd-0f2c9baaeb04 · outbound

This paper cites Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:46.763483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d112c86-658b-4fcd-a93a-57ffea1aba5b · outbound

This paper cites Gaussian differen- tial privacy,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Gaussian differen- tial privacy,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:48.482941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e443549c-b651-4ca3-9450-c1c3f949e365 · outbound

This paper cites Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:46.792095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ac5ffee-13df-4699-b66c-6440c551b5f9 · outbound

This paper cites Communication-efficient and Byzantine-robust differentially private federated learning,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Communication-efficient and Byzantine-robust differentially private federated learning,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:48.296775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 359de96d-e7e4-48a2-af0c-f5f483d291c3 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:48.115263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 027fbca9-5622-42e9-88ad-712b4bb2b4ac · outbound

This paper cites Efficient backprop,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Efficient backprop,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:47.986716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b46df620-d8d1-40e7-811d-e224aa855732 · outbound

This paper cites Exact solutions to the nonlinear dynamics of learning in deep linear neural networks.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:47.031882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15fb298a-3805-452d-9845-774e3bfbdee2 · outbound

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

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T04:17:47.095680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca5f2399-bf57-4507-ac23-d2b2784340f5 · outbound

This paper cites The MNIST database of handwritten digit images for machine learning research [best of the web],.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates The MNIST database of handwritten digit images for machine learning research [best of the web],

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:47.812449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3e5c5a32-9350-4aea-9ee2-56a085c2b25f · outbound

This paper cites CNN for handwritten Arabic digits recognition based on LeNet- 5,.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates CNN for handwritten Arabic digits recognition based on LeNet- 5,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:17:47.670469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 918cfff6-3acf-40be-b9cd-146e28f923f5 · outbound

This paper cites an unresolved cited work.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:17:47.510122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers