Joint Participation Incentive and Network Pricing Design for Federated Learning

TL;DR

This paper designs joint participation incentives and network pricing mechanisms for federated learning, optimizing resource management and user contribution while reducing server costs by up to 24.87%.

Contribution

It analytically derives optimal contracts and pricing mechanisms considering heterogeneous users and multi-dimensional decisions, addressing a complex multi-stage game.

Findings

Optimal contracts improve server cost efficiency.

Vertical interaction structure outperforms horizontal.

Proposed mechanisms reduce server costs by up to 24.87%.

Abstract

Federated learning protects users' data privacy through sharing users' local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users' contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users' private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under both vertical and horizontal interaction structures of the participants. We show that the vertical structure is better than the horizontal one, as it avoids the interests misalignment between the server and the network operator. Numerical results based on real-world datasets show that our proposed mechanisms decrease server's cost by up to 24.87% comparing with the state-of-the-art benchmarks.