Paper Citation Record · LEDGER

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning

As of 19 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2508.18060.

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

pith.paper-citation-record.v1
2508.18060 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:41:11.357890Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

24 of 24 outbound references displayed

verified exact0
verified fuzzy15
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation efaf7e0c-8e15-4e2c-aca8-c4aa9aeebfdd · outbound

This paper cites An overview of implementing security and privacy in federated learning,.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning An overview of implementing security and privacy in federated learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:12.303418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2caf1b57-b25e-4818-bc87-2270e067690f · outbound

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

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Federated learning: Challenges, methods, and future directions,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:12.161239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f5e843c8-7e53-4367-bc1f-2027bf585b32 · outbound

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

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Communication-efficient learning of deep networks from decentralized data,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:12.094205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 802e28c5-6a85-4cf7-8af9-2c7a287a6e60 · outbound

This paper cites The byzantine generals prob- lem,.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning The byzantine generals prob- lem,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:12.016021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9da8ddc8-b271-4f15-b5bd-701a88a9b8f5 · outbound

This paper cites A little is enough: Circumvent- ing defenses for distributed learning,.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning A little is enough: Circumvent- ing defenses for distributed learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:11.925715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9afc323f-c483-40c4-9691-b76c9ebaee03 · outbound

This paper cites Fall of empires: Breaking byzantine- tolerant sgd by inner product manipulation,.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Fall of empires: Breaking byzantine- tolerant sgd by inner product manipulation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:11.810012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b23336c8-a84f-4b6d-b057-8f9c4afefd05 · outbound

This paper cites Siren: Byzantine-robust federated learning via proactive alarming,.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Siren: Byzantine-robust federated learning via proactive alarming,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:11.691689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 59920740-d2df-4a05-b2a8-4acab1000b61 · outbound

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

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning An experimental study of byzantine- robust aggregation schemes in federated learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:11.571104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c9b397c7-0b1e-47d2-9e43-122633c1509f · outbound

This paper cites Local model poisoning attacks to {Byzantine-Robust} federated learning,.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Local model poisoning attacks to {Byzantine-Robust} federated learning,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T16:41:11.314588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f4c0a5b-c57f-45d0-a550-e8841ba02f31 · outbound

This paper cites Federated learning with extremely noisy clients via nega- tive distillation,.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Federated learning with extremely noisy clients via nega- tive distillation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:11.545992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8def7033-b631-49ed-98e2-3261b2317b72 · outbound

This paper cites Fedcor: Correlation-based active client selection strategy for heteroge- neous federated learning,.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Fedcor: Correlation-based active client selection strategy for heteroge- neous federated learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:11.526800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 719c9eb8-d4fd-478b-b47b-3e2a547e880e · outbound

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

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Byzantine-robust dis- tributed learning: Towards optimal statistical rates,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:11.512013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f9043d5f-e306-458d-b89f-c0e33f505f44 · outbound

This paper cites Ma- chine learning with adversaries: Byzantine tolerant gradient descent,.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Ma- chine learning with adversaries: Byzantine tolerant gradient descent,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:11.498220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2e7221ab-858b-4f6e-999e-e32a57582e5a · outbound

This paper cites Byzantine-robust federated learning through spatial-temporal analysis of local model updates,.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Byzantine-robust federated learning through spatial-temporal analysis of local model updates,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:11.482018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c63f17c6-d1d5-4cf6-9f30-c10840d93f8b · outbound

This paper cites FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T16:41:11.334303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 081bc29c-0835-4073-b5ad-8733fbe7e5d5 · outbound

This paper cites Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T16:41:11.337041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34bbe838-d472-4058-99da-aa6f6a14b540 · outbound

This paper cites Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T16:41:11.339830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a3da7a0-f505-448a-96ba-400f76ab91b1 · outbound

This paper cites Ensemble distillation for robust model fusion in federated learning,.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Ensemble distillation for robust model fusion in federated learning,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T16:41:11.342485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ef35f1d-dee6-4bbe-a54f-5bd40b9b009d · outbound

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

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Learning multiple layers of features from tiny images,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:11.460322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a8f9a35c-0e9a-414d-92fb-dc49f4f24e25 · outbound

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

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T16:41:11.347118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ec52f54-5bde-4a82-b428-0ac2e9cc6c12 · outbound

This paper cites Mnist handwritten digit database,.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Mnist handwritten digit database,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T16:41:11.349832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cd886dc-ca06-453b-ada4-94d25211fd34 · outbound

This paper cites Flower: A Friendly Federated Learning Research Framework.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Flower: A Friendly Federated Learning Research Framework

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T16:41:11.352635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9aa41c0a-d627-408a-b782-5860d442d21a · outbound

This paper cites Multi-task federated learning for person- alised deep neural networks in edge computing,.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Multi-task federated learning for person- alised deep neural networks in edge computing,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:41:11.440134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e6b11a63-784e-4662-a863-27c9e109c1ab · outbound

This paper cites Adaptive Federated Optimization.

FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning Adaptive Federated Optimization

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T16:41:11.357890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.