UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach

• URL: http://arxiv.org/abs/2106.15734v1
• Date: Tue, 29 Jun 2021 21:40:28 GMT
• Title: UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach
• Authors: Su Wang, Seyyedali Hosseinalipour, Maria Gorlatova, Christopher G. Brinton, Mung Chiang
• Abstract summary: We consider distributed machine learning (ML) through unmanned aerial vehicles (UAVs) for geo-distributed device clusters. We propose five new technologies/techniques: (i) stratified UAV swarms with leader, worker, and coordinator UAVs, (ii) hierarchical nested personalized federated learning (HN-PFL), and (iii) cooperative UAV resource pooling for distributed ML using the UAVs' local computational capabilities.
• Score: 25.936914508952086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider distributed machine learning (ML) through unmanned aerial vehicles (UAVs) for geo-distributed device clusters. We propose five new technologies/techniques: (i) stratified UAV swarms with leader, worker, and coordinator UAVs, (ii) hierarchical nested personalized federated learning (HN-PFL): a holistic distributed ML framework for personalized model training across the worker-leader-core network hierarchy, (iii) cooperative UAV resource pooling for distributed ML using the UAVs' local computational capabilities, (iv) aerial data caching and relaying for efficient data relaying to conduct ML, and (v) concept/model drift, capturing online data variations at the devices. We split the UAV-enabled model training problem as two parts. (a) Network-aware HN-PFL, where we optimize a tradeoff between energy consumption and ML model performance by configuring data offloading among devices-UAVs and UAV-UAVs, UAVs' CPU frequencies, and mini-batch sizes subject to communication/computation network heterogeneity. We tackle this optimization problem via the method of posynomial condensation and propose a distributed algorithm with a performance guarantee. (b) Macro-trajectory and learning duration design, which we formulate as a sequential decision making problem, tackled via deep reinforcement learning. Our simulations demonstrate the superiority of our methodology with regards to the distributed ML performance, the optimization of network resources, and the swarm trajectory efficiency.

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