TL;DR
This paper proposes a joint resource optimization and hyper-learning rate control framework for multiple federated learning services in mobile edge networks, improving efficiency and preserving privacy.
Contribution
It introduces the MS-FEDL model and develops both centralized and decentralized algorithms for resource and hyper-learning rate management in federated learning.
Findings
Decentralized algorithm converges effectively without revealing service info.
Proposed algorithms outperform heuristic strategies in simulations.
Joint optimization reduces energy consumption and learning time.
Abstract
Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices. In this paper, we study a new model of multiple federated learning services at the multi-access edge computing server. Accordingly, the sharing of CPU resources among learning services at each mobile device for the local training process and allocating communication resources among mobile devices for exchanging learning information must be considered. Furthermore, the convergence performance of different learning services depends on the hyper-learning rate parameter that needs to be precisely decided. Towards this end, we propose a joint resource optimization and hyper-learning rate control problem, namely MS-FEDL, regarding the energy consumption of mobile devices and overall learning time. We design a centralized algorithm based on the…
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