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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. [ABSTRACT FROM AUTHOR]

Recently, there has been a need for connectivity in places with no infrastructure. In order to meet this need, new technology known as MANET is used to fulfil the market demand. Despite the many benefits that MANET will provide, a number of shortcomings still need to be further studied, especially the energy consumption problems, that stand in the way of not allowing widespread acceptance of this technology. Because wireless devices use batteries with a finite amount of power, energy efficiency in these networks becomes a concern. In this paper, we present a design of energy aware on-demand routing protocol (EAORP), a new energy efficient algorithm that aims to overcome the shortcomings of DSR and provide a scalable traffic based and energy aware routing algorithm which aims to address energy issues in DSR by making it more aware and sensitive to nodes' energy, traffic loads, and transmission power management. [ABSTRACT FROM AUTHOR]