Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)

Abstract

The popularity of mobile devices results in the availability of enormous data and computational resources at the network edge. To leverage the data and resources, a new machine learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing fast and intelligent services to mobile users. While computing speeds are advancing rapidly, the communication latency is becoming the bottleneck of fast edge learning. To address this issue, this work is focused on designing a low latency multi-access scheme for edge learning. We consider a popular framework, federated edge learning (FEEL), where edge-server and on-device learning are synchronized to train a model without violating user-data privacy. It is proposed that model updates simultaneously transmitted by devices over broadband channels should be analog aggregated "over-the-air" by exploiting the superposition property of a multi-access channel. Thereby, "interference" is harnessed to provide fast implementation of the model aggregation. This results in dramatical latency reduction compared with the traditional orthogonal access (i.e., OFDMA). In this work, the performance of FEEL is characterized targeting a single-cell random network. First, due to power alignment between devices as required for aggregation, a fundamental tradeoff is shown to exist between the update-reliability and the expected update-truncation ratio. This motivates the design of an opportunistic scheduling scheme for FEEL that selects devices within a distance threshold. This scheme is shown using real datasets to yield satisfactory learning performance in the presence of high mobility. Second, both the multi-access latency of the proposed analog aggregation and the OFDMA scheme are analyzed. Their ratio, which quantifies the latency reduction of the former, is proved to scale almost linearly with device population.

arXiv commentThis is an extended version of a submission to IEEE journal

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Cited by in corpus (13)

• Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
• On Analog Gradient Descent Learning over Multiple Access Fading Channels
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey
• Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
• An Overview of Federated Learning at the Edge and Distributed Ledger Technologies for Robotic and Autonomous Systems
• Delay Minimization for Federated Learning Over Wireless Communication Networks
• Federated Learning via Over-the-Air Computation
• Wireless Federated Learning with Local Differential Privacy
• Gradient Statistics Aware Power Control for Over-the-Air Federated Learning
• Wireless for Machine Learning
• Federated Edge Learning : Design Issues and Challenges
• Optimized Power Control for Over-the-Air Computation in Fading Channels
• Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning