Authors: Han Zhang, Halvin Yang, Guopeng Zhang
Published on: August 26, 2023
Impact Score: 8.22
Arxiv code: Arxiv:2308.13838
Summary
- What is new: This research introduces a novel price-discrimination game in federated learning that accounts for the heterogeneity of client capabilities and their contribution to the learning process.
- Why this is important: The uniform pricing strategy for client participation in federated learning does not account for individual client characteristics or contributions, potentially undermining FL performance and cost-efficiency.
- What the research proposes: A price-discrimination game that differentiates pricing based on clients’ performance improvements and their computing and communication capabilities, aimed at enhancing FL performance and participation incentives.
- Results: The simulation shows that the proposed price-discrimination strategy effectively balances resource management, client selection, and incentive mechanisms, improving FL performance and cost-efficiency.
Technical Details
Technological frameworks used: Federated Learning, Price-Discrimination Game
Models used: Mixed-Integer Nonlinear Programming (MINLP)
Data used: Simulation-based
Potential Impact
Cloud computing and IoT device markets, particularly companies providing or leveraging federated learning services for distributed data processing and analytics.
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