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. To study this setting, we propose a potential game framework in which each client's payoff is determined by its individual effort and the rewards provided by the server. The rewards are influenced by the collective efforts of all clients and can be modulated by a reward factor. We first establish the existence of Nash
The paper addresses the growing complexity and market-oriented dynamics within federated learning environments, driven by the increasing need for decentralized AI training and resource allocation optimization.
This research provides a foundational framework to understand and incentivize client participation in federated learning, moving beyond traditional centralized control towards more efficient, self-interested models.
The shift from a centrally controlled to a game-theoretic model for federated learning introduces new methods for optimizing client contributions and reward mechanisms.
- · AI platform providers
- · Data privacy-focused sectors
- · Distributed computing infrastructure
- · Research institutions in game theory
- · Centralized AI training models
- · Organizations with rigid data governance
- · Inefficient federated learning setups
Improved efficiency and participation rates in federated learning networks.
Development of new incentive structures and marketplaces for distributed AI data and compute.
Accelerated adoption of federated learning across industries, potentially impacting data ownership and AI development paradigms.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG