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- W4380353807 abstract "Federated learning (FL) is a distributed paradigm that coordinates massive local clients to collaboratively train a global model via stage-wise local training processes on the heterogeneous dataset. Previous works have implicitly studied that FL suffers from the ``client-drift'' problem, which is caused by the inconsistent optimum across local clients. However, till now it still lacks solid theoretical analysis to explain the impact of this local inconsistency. To alleviate the negative impact of the ``client drift'' and explore its substance in FL, in this paper, we first design an efficient FL algorithm textit{FedInit}, which allows employing the personalized relaxed initialization state at the beginning of each local training stage. Specifically, textit{FedInit} initializes the local state by moving away from the current global state towards the reverse direction of the latest local state. This relaxed initialization helps to revise the local divergence and enhance the local consistency level. Moreover, to further understand how inconsistency disrupts performance in FL, we introduce the excess risk analysis and study the divergence term to investigate the test error of the proposed textit{FedInit} method. Our studies show that optimization error is not sensitive to this local inconsistency, while it mainly affects the generalization error bound in textit{FedInit}. Extensive experiments are conducted to validate this conclusion. Our proposed textit{FedInit} could achieve state-of-the-art~(SOTA) results compared to several advanced benchmarks without any additional costs. Meanwhile, stage-wise relaxed initialization could also be incorporated into the current advanced algorithms to achieve higher performance in the FL paradigm." @default.
- W4380353807 created "2023-06-13" @default.
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- W4380353807 date "2023-06-09" @default.
- W4380353807 modified "2023-09-27" @default.
- W4380353807 title "Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization" @default.
- W4380353807 doi "https://doi.org/10.48550/arxiv.2306.05706" @default.
- W4380353807 hasPublicationYear "2023" @default.
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