Gradient clipping eliminates the dependence on maximum delay in the oracle complexity of asynchronous SGD, yielding convergence in expectation and high probability under sub-Weibull gradient noise.
Fedml: A research li- brary and benchmark for federated machine learning
7 Pith papers cite this work, alongside 358 external citations. Polarity classification is still indexing.
7
Pith papers citing it
358
external citations · Pith
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.
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use method 1representative citing papers
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 oracular knowledge of client availability.
BlockTrain partitions models into block-local diffusion objectives that train near end-to-end WikiText quality with one-block worker memory, real WAN transport, and one-sweep distributed serving.
Adaptive bit-length schedulers plus Laplacian DP in non-IID FL reduce communicated data by up to 52.64% on MNIST and 45% on CIFAR-10 while keeping competitive accuracy and privacy.
HadAgent uses Proof-of-Inference consensus, a three-lane block structure, and a harness layer to enable secure decentralized LLM agent serving.
Multi-task autoencoders with outlier detection and federated SVDD loss filter noisy samples in non-IID federated learning, yielding accuracy gains up to 7% on CIFAR-10.
Benchmarks of MPI, gRPC, and PyTorch RPC in cross-silo FL plus a new gRPC+S3 hybrid backend deliver up to 3.8x speedup for large-model transmission under realistic network conditions.
citing papers explorer
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Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers
Gradient clipping eliminates the dependence on maximum delay in the oracle complexity of asynchronous SGD, yielding convergence in expectation and high probability under sub-Weibull gradient noise.
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Robust Federated Learning Under Real-World Client Churn
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 oracular knowledge of client availability.
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Decentralised AI Training and Inference with BlockTrain
BlockTrain partitions models into block-local diffusion objectives that train near end-to-end WikiText quality with one-block worker memory, real WAN transport, and one-sweep distributed serving.
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Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy
Adaptive bit-length schedulers plus Laplacian DP in non-IID FL reduce communicated data by up to 52.64% on MNIST and 45% on CIFAR-10 while keeping competitive accuracy and privacy.
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HadAgent: Harness-Aware Decentralized Agentic AI Serving with Proof-of-Inference Blockchain Consensus
HadAgent uses Proof-of-Inference consensus, a three-lane block structure, and a harness layer to enable secure decentralized LLM agent serving.
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Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data
Multi-task autoencoders with outlier detection and federated SVDD loss filter noisy samples in non-IID federated learning, yielding accuracy gains up to 7% on CIFAR-10.
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Understanding Communication Backends in Cross-Silo Federated Learning
Benchmarks of MPI, gRPC, and PyTorch RPC in cross-silo FL plus a new gRPC+S3 hybrid backend deliver up to 3.8x speedup for large-model transmission under realistic network conditions.