Over-the-Air Federated Multi-Task Learning
This work addresses communication bottlenecks for edge computing systems, though it is incremental as it builds on existing federated learning and compressed sensing methods.
The paper tackles the problem of communication inefficiency in federated multi-task learning by introducing an over-the-air computation framework, resulting in a significant reduction in channel uses without substantial performance degradation.
In this letter, we introduce over-the-air computation into the communication design of federated multi-task learning (FMTL), and propose an over-the-air federated multi-task learning (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES). Specifically, the model updates for all the tasks are transmitted and superimposed concurrently over a non-orthogonal uplink fading channel, and the model aggregations of all the tasks are reconstructed at the ES through a modified version of the turbo compressed sensing algorithm (Turbo-CS) that overcomes inter-task interference. Both convergence analysis and numerical results show that the OA-FMTL framework can significantly improve the system efficiency in terms of reducing the number of channel uses without causing substantial learning performance degradation.