Towards energy-efficient and time-sensitive task assignment in cross-silo federated learning

Abstract

Federated learning (FL) is a novel distributed learning framework in which clients with models can collaboratively train a high-quality global model while maintaining the privacy of their local data. The heterogeneity of datasets makes the global aggregation and local training requirements of each client in the cross-silo scenario inconsistent. Although many efforts are dedicated to improving the efficiency of cross-silo FL, the task assignment design of cross-silo FL remains an open question. To optimize the efficiency of energy consumption and training time of cross-silo FL, we develop a novel Energy-efficient and Time-sensitive Task Assignment (ETTA) mechanism. Specifically, we model clients’ requirements for energy efficiency and training time in cross-silo FL as an optimization problem, and design a utility transfer function to maintain the honesty of clients and the stability of ETTA. In terms of energy efficiency maximization, we analyze the conditions of optimal solutions based on the equilibrium of marginal cost and income, and design an approximate algorithm for cross-silo FL scenarios that require lightweight mechanisms. In terms of training time minimization, we model this objective as a min–max problem and give its analytical solution with Pareto optimality. To ensure that the task assignment achieves incentive compatibility, we further design a utility transfer selection function to incentivize honest collaboration among selfish clients. Finally, we conduct extensive experiments on the performance of ETTA to demonstrate that it outperforms the state-of-the-art and can maintain stability in a dishonest environment.

Keywords

• Cross-silo federated learning
• Task assignment
• Multi-objective optimization
• Incentive mechanism
• Game theory