FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants
Shanli Tan,
Hao Cheng,
Xiaohu Wu
et al.
Abstract:Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models. Given the potential data heterogeneity, it is crucial to select appropriate collaborators for each FL participant (FL-PT) based on data complementarity. Recent studies have addressed this challenge. Similarly, it is imperative to consider the inter-individual relationships among FL-PTs where some FL-PTs engage in competition. Although FL literature has acknowledged the significance of this scen… Show more
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