Federated Learning Can Find Friends That Are Beneficial
CoRR(2024)
摘要
In Federated Learning (FL), the distributed nature and heterogeneity of
client data present both opportunities and challenges. While collaboration
among clients can significantly enhance the learning process, not all
collaborations are beneficial; some may even be detrimental. In this study, we
introduce a novel algorithm that assigns adaptive aggregation weights to
clients participating in FL training, identifying those with data distributions
most conducive to a specific learning objective. We demonstrate that our
aggregation method converges no worse than the method that aggregates only the
updates received from clients with the same data distribution. Furthermore,
empirical evaluations consistently reveal that collaborations guided by our
algorithm outperform traditional FL approaches. This underscores the critical
role of judicious client selection and lays the foundation for more streamlined
and effective FL implementations in the coming years.
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