Auction-Based Incentive Mechanism in Federated Learning Considering Communication Path Finding

Abstract

Federated learning is a distributed learning approach which can protect clients’ privacy. The success of federated learning depends on the participation of high quality clients. However, many works neglect how to incentivize high quality clients to contribute to the model training. Furthermore, in existing works focusing on incentive mechanisms, the communication between the server and clients is unexplored. In this paper, we propose an auction-based incentive mechanism considering communication path finding to incentivize high quality clients’ participation. Specifically, we conduct the multi-criteria path finding to improve the communication between the server and clients. Then we take the communication parameters into the auction between the server and clients to select high quality clients for model training. When clients only have their own information, we design a distributed algorithm for clients to choose their optimal bids. Finally, numerical simulation results have been implemented to evaluate the efficiency of our proposed method.

Keywords

• Federated learning
• incentive mechanism
• auction mechanism
• multi-criteria path finding