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Federated learning model explores client self-interest and equilibrium transitions

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a potential game framework to model federated learning scenarios where clients act out of self-interest. This model analyzes how clients' rational choices in training efforts, influenced by server rewards, lead to Nash equilibria. The study reveals that these equilibria transition nonlinearly with a critical reward factor, potentially causing a shift between low and high effort levels among clients. The paper also validates the effectiveness of this critical factor in federated learning training. AI

RANK_REASON This is a research paper published on arXiv detailing a new model for federated learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Federated learning model explores client self-interest and equilibrium transitions

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This is a research paper published on arXiv detailing a new model for federated learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kang Liu, Ziqi Wang, Enrique Zuazua ·

    Nonlinear Equilibrium Transitions in a Potential Game Model for Federated Learning

    arXiv:2411.11793v2 Announce Type: replace Abstract: In federated learning (FL), a central server typically allocates training efforts to clients. However, from a market-oriented perspective, clients may independently choose their training efforts based on rational self-interest. …

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