A Federated Learning Latency Minimization Method for UAV Swarms Aided by Communication Compression and Energy Allocation

Unmanned aerial vehicle swarms (UAVSs) can carry out numerous tasks such as detection and mapping when outfitted with machine learning (ML) models. However, due to the flying height and mobility of UAVs, it is very difficult to ensure a continuous and stable connection between ground base stations and UAVs, as a result of which distributed machine learning approaches, such as federated learning (FL), perform better than centralized machine learning approaches in some circumstances when utilized by UAVs. However, in practice, functions that UAVs must perform often, such as emergency obstacle avoidance, require a high sensitivity to latency. This work attempts to provide a comprehensive analysis of energy consumption and latency sensitivity of FL in UAVs and present a set of solutions based on an efficient asynchronous federated learning mechanism for edge network computing (EAFLM) combined with ant colony optimization (ACO) for the cases where UAVs execute such latency-sensitive jobs. Specifically, UAVs participating in each round of communication are screened, and only the UAVs that meet the conditions will participate in the regular round of communication so as to compress the communication times. At the same time, the transmit power and CPU frequency of the UAV are adjusted to obtain the shortest time of an individual iteration round. This method is verified using the MNIST dataset and numerical results are provided to support the usefulness of our proposed method. It greatly reduces the communication times between UAVs with a relatively low influence on accuracy and optimizes the allocation of UAVs' communication resources.

Efficient Asynchronous Federated Learning for AUV Swarm

The development of automatic underwater vehicles (AUVs) has brought about unprecedented profits and opportunities. In order to discover the hidden valuable data detected by an AUV swarm, it is necessary to aggregate the data detected by AUV swarm to generate a powerful machine learning model. Traditional centralized machine learning generates a large number of data exchanges and faces problems of enormous training data, large-scale models, and communication. In underwater environments, radio waves are strongly absorbed, and acoustic communication is the only feasible technology. Unlike electromagnetic wave communication on land, the bandwidth of underwater acoustic communication is extremely limited, with the transmission rate being only 1/105 of the electromagnetic wave. Therefore, traditional centralized machine learning cannot support underwater AUV swarm training. In recent years, federated learning could only interact with model parameters without interacting with data, which greatly reduced communication costs. Therefore, this paper introduces federated learning into the collaboration of an AUV swarm. In order to further reduce the constraints of underwater scarce communication resources on federated learning and alleviate the straggler effect, in this work, we designed an asynchronous federated learning method. Finally, we constructed the optimization problem of minimizing the weighted sum of delay and energy consumption, relying on jointly optimizing the AUV CPU frequency and signal transmission power. In order to solve this complex optimization problem of high-dimensional non-convex time series accumulation, we transformed the problem into a Markov decision process (MDP) and use the proximal policy optimization 2 (PPO2) algorithm to solve this problem. The simulation results demonstrate the effectiveness and superiority of our method.