LEAF: A Benchmark for Federated Settings | Paper Citation Record

Observation fdfe475e-8ec5-41ef-b10a-96abefbb6fa0 · inbound

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

Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification LEAF: A Benchmark for Federated Settings

Reference 17

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Adaptive Federated Optimization cites this paper.

Adaptive Federated Optimization LEAF: A Benchmark for Federated Settings

Reference 209

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Incentivizing Honesty among Competitors in Collaborative Learning and Optimization cites this paper.

Incentivizing Honesty among Competitors in Collaborative Learning and Optimization LEAF: A Benchmark for Federated Settings

Reference 7

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Personalized Language Model Learning on Text Data Without User Identifiers cites this paper.

Personalized Language Model Learning on Text Data Without User Identifiers LEAF: A Benchmark for Federated Settings

Reference 3

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Distributed Quasi-Newton Method for Fair and Fast Federated Learning cites this paper.

Distributed Quasi-Newton Method for Fair and Fast Federated Learning LEAF: A Benchmark for Federated Settings

Reference 4

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THOR: A Generic Energy Estimation Approach for On-Device Training cites this paper.

THOR: A Generic Energy Estimation Approach for On-Device Training LEAF: A Benchmark for Federated Settings

Reference 2021

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Interaction-Aware Gaussian Weighting for Clustered Federated Learning cites this paper.

Interaction-Aware Gaussian Weighting for Clustered Federated Learning LEAF: A Benchmark for Federated Settings

Reference 2022

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Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks cites this paper.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks LEAF: A Benchmark for Federated Settings

Reference 2

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Observation 3ddb3446-6c30-447a-bdba-0aaf90ad41dc · inbound

Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning cites this paper.

Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning LEAF: A Benchmark for Federated Settings

Reference 9

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Observation dd7f2a67-c436-439b-accb-5a50f969f4a5 · inbound

Decoding FL Defenses: Systemization, Pitfalls, and Remedies cites this paper.

Decoding FL Defenses: Systemization, Pitfalls, and Remedies LEAF: A Benchmark for Federated Settings

Reference 17

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Observation 759940c4-f894-4115-ad75-8b6156a169b9 · inbound

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning cites this paper.

PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning LEAF: A Benchmark for Federated Settings

Reference 3

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Observation b365c047-ae33-4148-9c83-36d740f9b0f0 · inbound

A Survey on Foundation Models for Personalized Federated Intelligence cites this paper.

A Survey on Foundation Models for Personalized Federated Intelligence LEAF: A Benchmark for Federated Settings

Reference 141

Resolution: verified exact; arxiv_id, observed 2026-05-22T15:34:57.592497Z

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Observation 40a6c29a-a711-4c79-ad40-0a60a0cb95bf · inbound

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions cites this paper.

Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions LEAF: A Benchmark for Federated Settings

Reference 2018

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Observation 9be32b7b-4711-48a0-913c-e44080891a44 · inbound

Generalized and Personalized Federated Learning with Black-Box Foundation Models via Orthogonal Transformations cites this paper.

Generalized and Personalized Federated Learning with Black-Box Foundation Models via Orthogonal Transformations LEAF: A Benchmark for Federated Settings

Reference 5

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Observation 1ef97720-65ed-4097-b68a-4acfc473b56f · inbound

Accelerated Training of Federated Learning via Second-Order Methods cites this paper.

Accelerated Training of Federated Learning via Second-Order Methods LEAF: A Benchmark for Federated Settings

Reference 68

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Observation 4065d6df-b067-4a01-9268-f457595566b8 · inbound

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems cites this paper.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems LEAF: A Benchmark for Federated Settings

Reference 6

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Observation ca160a99-dcf7-4d15-8d7d-0a6cf923b8d1 · inbound

Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions cites this paper.

Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions LEAF: A Benchmark for Federated Settings

Reference 2019

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Observation 28336cf0-d820-499d-b2d0-db1168e0ca81 · inbound

HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark cites this paper.

HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark LEAF: A Benchmark for Federated Settings

Reference 5

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Observation 68dc5e3c-4d4b-48ce-9f99-9ee51f8cd146 · inbound

HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training cites this paper.

HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training LEAF: A Benchmark for Federated Settings

Reference 6

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Observation a057d532-3703-453c-978b-cbf1b12365e6 · inbound

FedAPM: Federated Learning via ADMM with Partial Model Personalization cites this paper.

FedAPM: Federated Learning via ADMM with Partial Model Personalization LEAF: A Benchmark for Federated Settings

Reference 12

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Observation 6981c97d-5a2a-4b81-8d9f-22d1ff339cc2 · inbound

Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection cites this paper.

Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection LEAF: A Benchmark for Federated Settings

Reference 19

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Observation abb31d6d-edd0-487a-af44-dbb1119292db · inbound

FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Data cites this paper.

FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Data LEAF: A Benchmark for Federated Settings

Reference 5

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Observation c09b88ee-4134-48ba-ae87-6b7bc84058fb · inbound

FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation cites this paper.

FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation LEAF: A Benchmark for Federated Settings

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-19T08:12:10.712066Z

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Observation 63a9f4a9-81a2-4f87-8e22-e0d3542fc6ba · inbound

Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources cites this paper.

Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources LEAF: A Benchmark for Federated Settings

Reference 24

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Observation 4eb03127-bd08-4be2-9db7-884ab67dcad5 · inbound

Federated Split Learning with Improved Communication and Storage Efficiency cites this paper.

Federated Split Learning with Improved Communication and Storage Efficiency LEAF: A Benchmark for Federated Settings

Reference 55

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Observation 4fc4a396-cf06-4e4a-848c-1db793780e4b · inbound

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges cites this paper.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges LEAF: A Benchmark for Federated Settings

Reference 145

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Observation b5c97601-afd5-461a-a710-9d4926db74f7 · inbound

Differentially Private Federated Clustering with Random Rebalancing cites this paper.

Differentially Private Federated Clustering with Random Rebalancing LEAF: A Benchmark for Federated Settings

Reference 5

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Observation 1def8fc6-14b0-4eb2-afcf-436d1c3cb05f · inbound

FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning cites this paper.

FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning LEAF: A Benchmark for Federated Settings

Reference 2

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FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks cites this paper.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks LEAF: A Benchmark for Federated Settings

Reference 43

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Observation f6cd5155-daf5-4573-840d-d926e073039f · inbound

Benchmarking Robust Aggregation in Decentralized Gradient Marketplaces cites this paper.

Benchmarking Robust Aggregation in Decentralized Gradient Marketplaces LEAF: A Benchmark for Federated Settings

Reference 2018

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Observation 0fca57a2-aa20-4bf5-907f-e7e940bd904b · inbound

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization cites this paper.

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization LEAF: A Benchmark for Federated Settings

Reference 26

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Observation 1dad18f0-8a11-4811-86c2-a62066b799dd · inbound

PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints cites this paper.

PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints LEAF: A Benchmark for Federated Settings

Reference 16

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Observation 8601a4d3-9e33-4e36-8064-df45141ee22b · inbound

SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening cites this paper.

SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening LEAF: A Benchmark for Federated Settings

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-18T08:56:08.499714Z

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Observation f0e81c96-fbc8-4fd8-bb08-d4979cd96197 · inbound

DFCA: Decentralized Federated Clustering Algorithm cites this paper.

DFCA: Decentralized Federated Clustering Algorithm LEAF: A Benchmark for Federated Settings

Reference 4

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Observation 2e93ee92-84d0-44fc-815d-62573e287bdd · inbound

Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration cites this paper.

Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration LEAF: A Benchmark for Federated Settings

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-17T01:53:51.047747Z

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Observation 2e68e94f-ae3d-4b8f-82d4-d3249a434166 · inbound

PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systems cites this paper.

PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systems LEAF: A Benchmark for Federated Settings

Reference 9

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Observation e2273c09-2183-4f40-8405-8cda05a6a88a · inbound

Task2vec Readiness: Diagnostics for Federated Learning from Pre-Training Embeddings cites this paper.

Task2vec Readiness: Diagnostics for Federated Learning from Pre-Training Embeddings LEAF: A Benchmark for Federated Settings

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:51:03.028148Z

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Observation 38b3bb16-e0e0-44ef-a80f-69d66a703cbd · inbound

PrivEraserVerify: Efficient, Private, and Verifiable Federated Unlearning cites this paper.

PrivEraserVerify: Efficient, Private, and Verifiable Federated Unlearning LEAF: A Benchmark for Federated Settings

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:36:14.867216Z

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Observation b771b0d9-6739-4134-9dc4-383aa8ae8878 · inbound

SMART: A Spectral Transfer Approach to Multi-Task Learning cites this paper.

SMART: A Spectral Transfer Approach to Multi-Task Learning LEAF: A Benchmark for Federated Settings

Reference 81

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Observation b421009e-6c48-437d-9b78-f8ea46bfd586 · inbound

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy cites this paper.

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy LEAF: A Benchmark for Federated Settings

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:16:08.231877Z

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Observation e15ecb3b-1b5b-446a-8d61-955207fa467c · inbound

FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices cites this paper.

FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices LEAF: A Benchmark for Federated Settings

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:31:15.914030Z

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Observation 6562ea13-6911-4b26-9a95-84f7e33757b4 · inbound

FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices cites this paper.

FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices LEAF: A Benchmark for Federated Settings

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:37:29.591813Z

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Observation 89f74fca-d9d2-4261-9f22-84bb5d3f59ae · inbound

FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices cites this paper.

FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices LEAF: A Benchmark for Federated Settings

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-07-04T01:49:21.563625Z

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Observation a03ff947-abd8-4400-a5b7-ddbd4a3341ce · inbound

From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning cites this paper.

From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning LEAF: A Benchmark for Federated Settings

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:51:10.810845Z

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Observation 1b46ca56-27f9-44af-9d67-544924272003 · inbound

FedFrozen: Two-Stage Federated Optimization via Attention Kernel Freezing cites this paper.

FedFrozen: Two-Stage Federated Optimization via Attention Kernel Freezing LEAF: A Benchmark for Federated Settings

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T19:01:19.612335Z

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Observation ca69f770-24ae-42f0-9231-8d726e0ca2b4 · inbound

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity cites this paper.

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity LEAF: A Benchmark for Federated Settings

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T19:32:50.974210Z

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Observation c84e5a03-b9fe-4b33-b5e9-03701ad70099 · inbound

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits cites this paper.

Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits LEAF: A Benchmark for Federated Settings

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T01:53:29.078725Z

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Observation 6d5f4e8a-c35e-41e0-bf27-cd62e517770f · inbound

Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning cites this paper.

Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning LEAF: A Benchmark for Federated Settings

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-20T07:38:09.565766Z

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Observation dc829208-38a3-4894-8406-35da74fa273a · inbound

Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning cites this paper.

Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning LEAF: A Benchmark for Federated Settings

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:24:59.972511Z

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Observation 6f30b939-91ad-4299-9521-38693284c24a · inbound

CRAFT: Conflict-Resolved Aggregation for Federated Training cites this paper.

CRAFT: Conflict-Resolved Aggregation for Federated Training LEAF: A Benchmark for Federated Settings

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T05:49:40.863706Z

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Observation c96ee16e-837f-4197-a621-be6800ce89f9 · inbound

Totoro$^+$: An Adaptive and Scalable Edge Federated Learning System cites this paper.

Totoro$^+$: An Adaptive and Scalable Edge Federated Learning System LEAF: A Benchmark for Federated Settings

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-01T16:55:50.548354Z

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Observation 4dc2375d-8b50-4ab1-9f5a-1b48f18caf61 · inbound

Silent Failures in Federated Personalization of Foundation Models cites this paper.

Silent Failures in Federated Personalization of Foundation Models LEAF: A Benchmark for Federated Settings

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T18:02:26.222223Z

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Observation 44422bc1-3c24-413f-8be0-59e0f6a1f61e · inbound

FedMTFI: Feature Importance Based Optimized Multi Teacher Knowledge Distillation in Heterogeneous Federated Learning Environment cites this paper.

FedMTFI: Feature Importance Based Optimized Multi Teacher Knowledge Distillation in Heterogeneous Federated Learning Environment LEAF: A Benchmark for Federated Settings

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-01T22:06:15.804676Z

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Observation 2c4a05e7-312d-4acd-9873-bf4120129991 · inbound

Closing the Alignment-Maturity Gap in Federated Prototype Learning cites this paper.

Closing the Alignment-Maturity Gap in Federated Prototype Learning LEAF: A Benchmark for Federated Settings

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-01T21:56:16.423464Z

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Observation 733a0f76-2d53-48cb-841f-313068ec5f7a · inbound

Exploring CKKS Parameter Trade-offs for Privacy-Preserving Personalized Federated Learning cites this paper.

Exploring CKKS Parameter Trade-offs for Privacy-Preserving Personalized Federated Learning LEAF: A Benchmark for Federated Settings

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:37:27.351108Z

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 67e26967-529b-413e-889d-92267014d6d7 · inbound

pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning cites this paper.

pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning LEAF: A Benchmark for Federated Settings

Reference 43

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Unavailable: canonical work link unavailable.

Observation c122ea5b-317a-4f58-a95e-faf826d69544 · inbound

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems cites this paper.

Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems LEAF: A Benchmark for Federated Settings

Reference 116

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:00:07.845935Z

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 aaae2aa9-f416-412e-95fb-4dc7e4b35cb9 · inbound

FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning cites this paper.

FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning LEAF: A Benchmark for Federated Settings

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-01T09:55:41.357182Z

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 dc795426-baee-4eb6-ba77-cbaa5605edbf · inbound

Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data cites this paper.

Federated Learning for Object Detection: Enabling Collaborative Drone Learning Without Centralizing Data LEAF: A Benchmark for Federated Settings

Reference 30

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Observation 5e245410-3b2c-4986-8f5b-b0876e1550d4 · inbound

FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering cites this paper.

FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering LEAF: A Benchmark for Federated Settings

Reference 47

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Unavailable: canonical work link unavailable.

Observation 23e70ba4-5ab8-4513-aa24-d3dd6ca5fa00 · inbound

SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity cites this paper.

SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity LEAF: A Benchmark for Federated Settings

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-11T21:05:03.994941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55e3e192-fdd6-4780-8726-753b80b64180 · inbound

PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning cites this paper.

PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning LEAF: A Benchmark for Federated Settings

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-07-11T01:57:52.301024Z

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 f2604187-4c35-4345-817d-771b5fae4c38 · inbound

HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning cites this paper.

HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning LEAF: A Benchmark for Federated Settings

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-13T07:32:39.495351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2604652-6362-45ba-9b4b-03a47f9d85c3 · inbound

NFSA: Non-Forward Secure Aggregation with One Server via Two Layer Secret Sharing cites this paper.

NFSA: Non-Forward Secure Aggregation with One Server via Two Layer Secret Sharing LEAF: A Benchmark for Federated Settings

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T00:29:02.071069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f626d505-2e12-4b89-bff8-143c3d80ab8d · inbound

AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning cites this paper.

AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning LEAF: A Benchmark for Federated Settings

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T16:41:36.776273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c4f790b-babc-4aa7-b6e0-d8c0c247445f · inbound

PIcsC: Partitioning-Induced Covariate Shift Correction cites this paper.

PIcsC: Partitioning-Induced Covariate Shift Correction LEAF: A Benchmark for Federated Settings

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-01T02:30:36.379602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f44cd088-cc9f-4db2-9969-20a237e15a57 · inbound

Reputation-driven Cooperation in Lattice-based Decentralized Federated Learning through Evolutionary Game Theory cites this paper.

Reputation-driven Cooperation in Lattice-based Decentralized Federated Learning through Evolutionary Game Theory LEAF: A Benchmark for Federated Settings

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T00:30:52.792258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4620ed8a-f24e-469d-8bb6-376bef9dc983 · inbound

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning cites this paper.

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning LEAF: A Benchmark for Federated Settings

Reference 2021

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

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Outbound references

Pith citing papers