Toward a Sustainable Federated Learning Ecosystem: A Practical Least Core Mechanism for Payoff Allocation
Zhengwei Ni, Zhidu Li, Wei Chen, Zhaoyang Zhang, Zehua Wang, F. Richard Yu, Victor C. M. Leung

TL;DR
This paper proposes a practical payoff allocation mechanism for federated learning based on the least core concept, ensuring coalition stability and promoting sustainability in collaborative environments.
Contribution
It introduces a scalable, game-theoretic least core framework with a stack-based algorithm for fair payoff distribution in federated learning.
Findings
The mechanism ensures stable coalition formation.
It accurately identifies key contributors and alliances.
The approach promotes sustainable federated learning ecosystems.
Abstract
Emerging network paradigms and applications increasingly rely on federated learning (FL) to enable collaborative intelligence while preserving privacy. However, the sustainability of such collaborative environments hinges on a fair and stable payoff allocation mechanism. Focusing on coalition stability, this paper introduces a payoff allocation framework based on the least core (LC) concept. Unlike traditional methods, the LC prioritizes the cohesion of the federation by minimizing the maximum dissatisfaction among all potential subgroups, ensuring that no participant has an incentive to break away. To adapt this game-theoretic concept to practical, large-scale networks, we propose a streamlined implementation with a stack-based pruning algorithm, effectively balancing computational efficiency with allocation precision. Case studies in federated intrusion detection demonstrate that our…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Mobile Crowdsensing and Crowdsourcing · Stochastic Gradient Optimization Techniques
