UAV-Aided Multi-Community Federated Learning
In this work, we investigate the problem of an online trajectory design for an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where several different communities exist, each defined by a unique task to be learned. In this setting, spatially distributed devices belonging to each community collaboratively contribute towards training their community model via wireless links provided by the UAV. Accordingly, the UAV acts as a mobile orchestrator coordinating the transmissions and the learning schedule among the devices in each community, intending to accelerate the learning process of all tasks. We propose a heuristic metric as a proxy for the training performance of the different tasks. Capitalizing on this metric, a surrogate objective is defined which enables us to jointly optimize the UAV trajectory and the scheduling of the devices by employing convex optimization techniques and graph theory. The simulations illustrate the out-performance of our solution when compared to other handpicked static and mobile UAV deployment baselines.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningSchedulingSimilar Papers 제목 키워드 기반
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks
Deploying teams of unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorithms. Multi-agent reinforcement learning …
Federated LearningMulti-agent Reinforcement LearningTrajectory PlanningIRS Aided Federated Learning: Multiple Access and Fundamental Tradeoff
This paper investigates an intelligent reflecting surface (IRS) aided wireless federated learning (FL) system, where an access point (AP) coordinates multiple edge devices to train a machine leaning model without sharing…
Federated LearningSchedulingDiffusion Model-Based Data Synthesis Aided Federated Semi-Supervised Learning
Federated semi-supervised learning (FSSL) is primarily challenged by two factors: the scarcity of labeled data across clients and the non-independent and identically distribution (non-IID) nature of data among clients. I…
Federated LearningPost-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing
Federated Learning (FL) is a distributed machine learning framework in which a set of local communities collaboratively learn a shared global model while retaining all training data locally within each community. Two not…
FairnessFederated LearningSynthetic Data Aided Federated Learning Using Foundation Models
In heterogeneous scenarios where the data distribution amongst the Federated Learning (FL) participants is Non-Independent and Identically distributed (Non-IID), FL suffers from the well known problem of data heterogenei…
Data AugmentationFederated Learning