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arXiv · 2611.03024

DisC: Incentive Design for Green, Robust, and Fair Client Self-Participation in OTA Federated Learning

Driss Choukri·Essaid Sabir·Abdelkrim Haqiq
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Abstract

Over-the-Air Federated Learning (OTA-FL) improves communication efficiency by exploiting waveform superposition over wireless Multiple-Access Channels (MAC). However, client heterogeneity, fading, energy constraints, and participation imbalance can degrade both fairness and aggregation accuracy. Existing Pareto-oriented fair OTA-FL schemes may still suffer from large aggregation distortion when highly weighted clients experience poor channel conditions. This paper proposes DisC, a game-theoretic distributed client selection framework for fairness-aware, channel-aware, and energy-efficient OTA-FL. Each client selects its participation level by maximizing a utility that captures Pareto fairness importance, learning relevance, OTA distortion, energy consumption, and delay. We show that DisC admits a Nash equilibrium and can be formulated as an exact potential game, where stable equilibria correspond to locally optimal OTA-FL operating points. DisC therefore offers a distributed, incentive-compatible alternative to centralized client selection under wireless and resource constraints.

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Driss Choukri, Essaid Sabir, Abdelkrim Haqiq. 2026-11-02. DisC: Incentive Design for Green, Robust, and Fair Client Self-Participation in OTA Federated Learning. https://arxiv.org/abs/2611.03024

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