An energy-efficient transmission and computation resource allocation problem for federated learning (FL) on wireless communication networks is investigated. Based on the considered model, each user exploits limited local computing resources to train a local FL model with its collected data. The local FL model is then transmitted to a base station (BS), which aggregates the local FL model and broadcasts it back to all users. Based on the learning accuracy level, computation and communication latency are determined by the exchange of learning models between users and BS. During the FL process, both the local computation energy and the transmission energy must be considered. Due to wireless users’ limited energy consumption, the communication problem is formulated as an optimization problem whose objective is to minimize the overall energy consumption of the system with a latency limitation. To solve this problem, we resort to an iterative algorithm with a solution of bandwidth, power, computational and other factors. Numerical results show that the proposed algorithms can reduce energy consumption compared to the conventional FL method.
The article explores an energy-efficient method for allocating transmission and computation resources for federated learning (FL) on wireless communication networks. The model being considered involves each user training a local FL model using their limited local computing resources and the data they have collected. These local models are then transmitted to a base station, where they are aggregated and broadcast back to all users. The level of accuracy in learning, as well as computation and communication latency, are determined by the exchange of models between users and the base station. Throughout the FL process, energy consumption for both local computation and transmission must be taken into account. Given the limited energy resources of wireless users, the communication problem is formulated as an optimization problem with the goal of minimizing overall system energy consumption while meeting a latency requirement. To address this problem, we propose an iterative algorithm that takes into account factors such as bandwidth, power, and computational resources. Results from numerical simulations demonstrate that the proposed algorithm can reduce energy consumption compared to traditional FL methods up to 51% reduction.
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