FedML: A Research Library and Benchmark for Federated Machine Learning

By Chaoyang He, Songze Li, Jinhyun So, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Li Shen, Peilin Zhao and 6 more. 2020

716 citations recorded by the source corpus.

Federated learning is a rapidly growing research field in the machine learning domain. Although considerable research efforts have been made, existing libraries cannot adequately support diverse algorithmic development (e.g., diverse topology and flexible message exchange), and inconsistent dataset and model usage in experiments make fair comparisons difficult. In this work, we introduce FedML, an open research library and benchmark that facilitates the development of new federated learning algorithms and fair performance comparisons. FedML supports three computing paradigms (distributed training, mobile on-device training, and standalone simulation) for users to conduct experiments in different system environments. FedML also promotes diverse algorithmic research with flexible and generic API design and reference baseline implementations. A curated and comprehensive benchmark dataset for the non-I.I.D setting aims at making a fair comparison. We believe FedML can provide an efficient and reproducible means of developing and evaluating algorithms for the federated learning research community. We maintain the source code, documents, and user community at this https URL.

Subfields: Artificial Intelligence, Information Systems, Evaluation & Benchmarks, Federated & Distributed Learning

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Citations in the corpus

Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning (2020, methodology): “Platform: In our experiments, we implement TurboAggregate on a distributed platform by using FedML library [50], and examine its total running time with respect to the state-of-the-art [3].”
Group Knowledge Transfer: Collaborative Training of Large CNNs on the Edge (2020, methodology): “Thus, to simplify the usage of our GKT training framework, we use FedML [55], an open source federated learning library that simplifies the new algorithm development, and deploy it in a distributed computing environment.”
From distributed machine learning to federated learning: a survey (2021, background): “in order to analyze the performance of the trained FL models on a simulation platform or a real distributed system [65].”
FedProto: Federated Prototype Learning across Heterogeneous Clients (2021, background): “The recently proposed KD-based FL (Lin et al. 2020; Jeong et al. 2018; Li and Wang 2020; Long et al. 2021) can serve as an alternative solution to address this challenge.”
FairFed: Enabling Group Fairness in Federated Learning (2021, positive): “We developed FairFed using FedML (He et al. 2020), which is a research-friendly FL library for exploring new algorithms.”
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale (2021, background): “As shown in Figure 8 3, FAR not only runs faster than FedML [28] (using 10 clients per round), thus saving lots of GPU hours, but can support large-scale evaluations efficiently.”
Model-Contrastive Federated Learning (2021, background): “Federated learning has emerged as an important machine learning area and attracted many research interests [34, 28, 22, 25, 41, 5, 16, 2, 11].”
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019, methodology): “Using realistic partitioning method, as proposed in FedML [68] and OARF [70] may mitigate this issue, but for federated learning at a large-scale, realistic partitioning is not suitable due to the difficulty of collecting data from different sources.”
Heterogeneous Federated Learning: State-of-the-art and Research Challenges (2023, background): “FedML [73] is a research library that supports distributed training, mobile on-device training, and stand-alone simulation training.”
Trustworthy AI: From Principles to Practices (2021, methodology): “To expedite FL-related research and development, many open source libraries have been released, such as FATE, FedML [149], and FedlearnAlgo [217].”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, background): “To further accelerate research progress and facilitate algorithmic innovation and performance comparison in realistic FL environments, FedML [109], a research-oriented open FL library, has recently been established to support diverse FL computing environments and topological architectures with…”
Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges (2022, methodology): “Other frameworks, such as FederatedScope [145] and FedML [146], allow lower-level management of the deployed architecture.”