Pith. sign in

REVIEW 21 cited by

FedML: A Research Library and Benchmark for Federated Machine Learning

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2007.13518 v4 pith:N2MAPFJ2 submitted 2020-07-27 cs.LG stat.ML

Chaoyang He , Songze Li , Jinhyun So , Xiao Zeng , Mi Zhang , Hongyi Wang , Xiaoyang Wang , Praneeth Vepakomma
show 12 more authors
Abhishek Singh Hang Qiu Xinghua Zhu Jianzong Wang Li Shen Peilin Zhao Yan Kang Yang Liu Ramesh Raskar Qiang Yang Murali Annavaram Salman Avestimehr
This is my paper · ORCID
classification cs.LGstat.ML
keywords fedmlresearchlearningalgorithmalgorithmicbenchmarkcommunitycomparison
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsistent dataset and model usage make fair algorithm comparison challenging. In this work, we introduce FedML, an open research library and benchmark to facilitate FL algorithm development and fair performance comparison. FedML supports three computing paradigms: on-device training for edge devices, distributed computing, and single-machine simulation. FedML also promotes diverse algorithmic research with flexible and generic API design and comprehensive reference baseline implementations (optimizer, models, and datasets). We hope FedML could provide an efficient and reproducible means for developing and evaluating FL algorithms that would benefit the FL research community. We maintain the source code, documents, and user community at https://fedml.ai.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 21 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 358 citations worldwide. Full citation record

  1. Understanding Developer Pain Points in Federated Learning: Insights from Stack Overflow and GitHub

    cs.SE 2026-07 conditional novelty 6.0 of 10

    Federated-learning developers' biggest public pain points are environment setup, API/version breakage, non-IID training instability, and evaluation/privacy integration, with Stack Overflow skewing How and GitHub skewing Why.

  2. Robust Federated Learning Under Real-World Client Churn

    cs.LG 2026-07 conditional novelty 6.0 of 10

    FeLiX reduces wall-clock time-to-target accuracy in federated learning by up to 2.37x using lightweight availability tiers, fresh-utility client selection, and informativeness-aware aggregation without requiring oracu...

  3. Decentralised AI Training and Inference with BlockTrain

    cs.AI 2026-06 unverdicted novelty 6.0 of 10

    BlockTrain partitions models into blocks trained on local objectives, reaching CE 1.359 on WikiText within 0.04 of end-to-end baseline while enabling distributed training and inference over TCP for up to 75B-parameter models.

  4. Benchmarking Robust Aggregation in Decentralized Gradient Marketplaces

    cs.LG 2025-09 conditional novelty 6.0 of 10

    Adaptive Sybil backdoor attacks can defeat MartFL, FLTrust, and SkyMask in buyer-baseline gradient marketplaces with little visible effect on accuracy or cost.

  5. Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources

    cs.DC 2025-07 conditional novelty 6.0 of 10

    Flotilla is a modular, resilient federated learning framework that runs on heterogeneous edge devices, supports sync and async strategies, and scales to 1000+ clients with low overhead.

  6. FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models

    cs.LG 2025-06 conditional novelty 6.0 of 10

    FLoRIST aggregates federated LoRA adapters through SVD in a small intermediate space and thresholding, producing compact global adapters with up to hundreds-fold lower download cost at comparable MMLU accuracy.

  7. HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark

    cs.LG 2025-06 conditional novelty 6.0 of 10

    HtFLlib is a unified benchmark and library with 12 datasets, 40 heterogeneous model architectures, and systematic accuracy, convergence, and cost evaluations of 10 HtFL methods.

  8. ByzFL: Research Framework for Robust Federated Learning

    cs.LG 2025-05 conditional novelty 6.0 of 10

    The paper introduces ByzFL, a modular open-source library for simulating, attacking, and benchmarking Byzantine-robust federated learning with a single JSON config.

  9. FNBench: Benchmarking Robust Federated Learning against Noisy Labels

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A unified benchmark of 18 federated learning methods under synthetic, human-annotation, and systematic label noise finds no consistently winning method and shows that an SVD-based decorrelation regularizer improves mo...

  10. Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

    cs.LG 2024-11 conditional novelty 6.0 of 10

    A comprehensive survey that organizes non-IID data in federated learning into taxonomies of skew types, partition protocols, and metrics, with a meta-analysis of 235 selected papers.

  11. FraQ: Efficient Coordinate-Space Recompression for Federated Low-Rank Adaptation

    cs.AI 2026-08 conditional novelty 5.0 of 10

    FRAQ recovers the rank-truncated exact aggregate of client LoRA updates using one reduced QR and a small Gram eigenproblem, avoiding any full-weight-space SVD.

  12. FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning

    cs.DC 2025-07 conditional novelty 5.0 of 10

    FLsim is a modular, library-agnostic federated learning simulation framework supporting custom strategies, multiple topologies, and pluggable blockchain consensus.

  13. Distributionally Robust Federated Learning with Client Drift Minimization

    cs.LG 2025-05 reject novelty 5.0 of 10

    DRDM combines DRO and FedDyn-style dynamic regularization to improve worst-case client accuracy in federated learning, with a claimed O(1/T^{3/8}) duality-gap convergence rate.

  14. SVAFD: A Secure and Verifiable Co-Aggregation Protocol for Federated Distillation

    cs.CR 2025-05 reject novelty 5.0 of 10

    SVAFD signs per-slice sums of logits, not the logits themselves, so a malicious server can change the teacher knowledge without breaking the proof.

  15. Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data

    cs.LG 2025-05 conditional novelty 5.0 of 10

    pFedLIA clusters federated learning clients using a cheap 'lazy influence' score, and on CIFAR10/100 and FashionMNIST it matches a perfect-clustering oracle while improving on existing personalized FL baselines by up ...

  16. Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection

    cs.LG 2024-12 conditional novelty 5.0 of 10

    Selecting the local examples a model is least confident about, and training only part of the model, lets federated learning use far less client data and compute while keeping or improving accuracy.

  17. DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices

    cs.LG 2024-12 conditional novelty 5.0 of 10

    A federated learning method combining model-fusion pruning with representation regularization reports modest accuracy gains on two benchmarks while compressing models for heterogeneous devices.

  18. PyG 2.0: Scalable Learning on Real World Graphs

    cs.LG 2025-07 conditional novelty 4.0 of 10

    PyG 2.0 is presented as a modular, scalable graph-learning framework with heterogeneous and temporal graph support, compilation-based speedups, and explainability.

  19. Towards a Distributed Federated Learning Aggregation Placement using Particle Swarm Intelligence

    cs.DC 2025-04 conditional novelty 4.0 of 10

    A particle swarm optimizer that places aggregation nodes in federated learning based only on total processing delay converges quickly and beats random and round-robin placement in simulation and on a Docker testbed.

  20. Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration

    cs.DC 2025-01 conditional novelty 4.0 of 10

    FedEEC trains larger cloud models in end-edge-cloud federated learning via bridge-sample distillation and self-rectified knowledge transfer, reporting accuracy gains on SVHN, CIFAR-10, and CINIC-10.

  21. Modular Federated Learning: A Meta-Framework Perspective

    cs.LG 2025-05 conditional novelty 3.0 of 10

    A 63-page survey that reframes federated learning as a composition of eight modules and proposes an 'alignment operator' taxonomy, while surveying Python FL frameworks and open challenges.

Pith tools