A Decentralized Game Theoretic Approach for Energy-aware Resource Management in Federated Learning

The resource management in Federated Learning (FL) system has been a challenging issue since mobile users can save the energy consumption by limiting their computing resources and dataset in training their local models in which users have the energy limitation. We analyze the performance of the global model on the size of dataset and computing resources used for the local training. The performance of the final model is significantly influenced by the resource management of users. Moreover, the decisions of the users on the communication, computing resources and size of dataset can affect the time taken for one computing round. Since a large number of mobile users participate in the FL, a centralized resource management is not practical. Thus, we formulate an energy-aware resource management problem for FL in which users are interested in minimizing the time taken for one computing round with the constraints of energy consumption, communication resources and performance of the training model. Due to the coupling in the communication resource allocation, we formulate the resource management problem as a Generalized Nash Equilibrium Problem (GNEP) and propose a decentralized algorithm. In addition, we analyze the performance of the proposed algorithm on the resource management, energy and time consumption.

[1]  H. Vincent Poor,et al.  Convergence Time Optimization for Federated Learning Over Wireless Networks , 2020, IEEE Transactions on Wireless Communications.

[2]  Mehdi Bennis,et al.  Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation , 2020, 2020 IEEE Wireless Communications and Networking Conference (WCNC).

[3]  Choong Seon Hong,et al.  Cost and Latency Tradeoff in Mobile Edge Computing: A Distributed Game Approach , 2019, 2019 IEEE International Conference on Big Data and Smart Computing (BigComp).

[4]  Walid Saad,et al.  Energy Efficient Federated Learning Over Wireless Communication Networks , 2019, IEEE Transactions on Wireless Communications.

[5]  Zhu Han,et al.  Generalized Nash Equilibrium Game for Radio and Computing Resource Allocation in Co-located MEC , 2020, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).

[6]  Solmaz Niknam,et al.  Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges , 2019, IEEE Communications Magazine.

[7]  Navid Naderializadeh.  On the Communication Latency of Wireless Decentralized Learning , 2020, ArXiv.

[8]  Albert Y. Zomaya,et al.  Federated Learning over Wireless Networks: Optimization Model Design and Analysis , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[9]  Walid Saad,et al.  A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks , 2021, IEEE Transactions on Wireless Communications.

[10]  Mugen Peng,et al.  Joint Optimization of Data Sampling and User Selection for Federated Learning in the Mobile Edge Computing Systems , 2020, 2020 IEEE International Conference on Communications Workshops (ICC Workshops).

[11]  Richard Nock,et al.  Advances and Open Problems in Federated Learning , 2021, Found. Trends Mach. Learn..