☆ 4.7 Article

Knowledge Selection and Local Updating Optimization for Federated Knowledge Distillation With Heterogeneous Models

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (2023)

Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing

Jed Mills et al.

Summary: Federated Learning (FL) is an emerging approach that allows collaborative training of Deep Neural Networks (DNNs) on mobile devices without the need for data leaving the devices. We propose a Multi-Task Federated Learning (MTFL) algorithm that introduces non-federated Batch-Normalization (BN) layers to improve individual user model accuracy (UA) and convergence speed. Experimental results demonstrate significant reduction in the number of rounds required to reach the target UA using our algorithm on MNIST and CIFAR10 datasets.

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS (2022)

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Article Engineering, Electrical & Electronic

A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

Mingzhe Chen et al.

Summary: This article discusses the challenges of training federated learning algorithms over a realistic wireless network and proposes an optimization model to minimize the FL loss function, providing a method to improve identification accuracy.

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2021)

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Article Computer Science, Hardware & Architecture