Latency-Aware Semi-Synchronous Client Selection and Model Aggregation for Wireless Federated Learning

Liangkun Yu, Xiang Sun (), Rana Albelaihi and Chen Yi
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Liangkun Yu: SECNet Laboratory, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
Xiang Sun: SECNet Laboratory, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
Rana Albelaihi: SECNet Laboratory, Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
Chen Yi: Chongqing Key Laboratory of Signal and Information Processing, School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Future Internet, 2023, vol. 15, issue 11, 1-15

Abstract: Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in the context of various applications, e.g., next-word prediction and eHealth. FL involves various clients participating in the training process by uploading their local models to an FL server in each global iteration. The server aggregates these models to update a global model. The traditional FL process may encounter bottlenecks, known as the straggler problem, where slower clients delay the overall training time. This paper introduces the Latency-awarE Semi-synchronous client Selection and mOdel aggregation for federated learNing (LESSON) method. LESSON allows clients to participate at different frequencies: faster clients contribute more frequently, therefore mitigating the straggler problem and expediting convergence. Moreover, LESSON provides a tunable trade-off between model accuracy and convergence rate by setting varying deadlines. Simulation results show that LESSON outperforms two baseline methods, namely FedAvg and FedCS, in terms of convergence speed and maintains higher model accuracy compared to FedCS.

Keywords: federated learning; client selection; model aggregation; semi-synchronous; IoT (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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