Jun 21, 2021 · Abstract:Federated Learning is a distributed machine learning approach which enables model training without data sharing.
Jun 21, 2021 · We propose FedCM, a novel, efficient and robust federated optimization algorithm, in which the server maintains a momentum term to guide client ...
Jun 21, 2021 · This paper proposes a new federated learning algorithm, Federated Averaging with Client-level Momentum (FedCM), to tackle problems of ...
Fedcm: Federated learning with client-level momentum. J Xu, S Wang, L Wang ... Bilevel Optimization without Lower-Level Strong Convexity from the Hyper-Objective ...
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Feb 1, 2023 · This paper presents a communication-efficient federated optimization algorithm that deals with client heterogeneity under large-scale and low- ...
Mar 22, 2024 · This approach allows for high precision while significantly reducing the data transmitted during client-server interactions. We provide ...
Comparison with FedAvgM and FedCM To better un- derstand the effectiveness of the accelerated client gradient, we compare two momentum-based algorithms, FedAvgM.
Federated Learning (FL) is a distributed machine learning approach that enables model training in communication efficient and privacy-preserving manner. The ...
Jun 21, 2021 · Federated Learning is a distributed machine learning approach which enables model training without data sharing. In this paper, we propose a ...
To address these issues, we propose a novel federated learning algorithm, named FedMIM, which adopts the multi-step inertial momentum on the edge devices and ...