arXiv:2608. 17849v1 Announce Type: new Abstract: Split federated learning (SFL) has emerged as a powerful paradigm for model training at the edge.
arXiv:2302. 09832v4 Announce Type: replace Abstract: In distributed optimization and federated learning, slow and costly communication between parallel devices and the central server constitutes the primary bottleneck.
arXiv:2607. 29659v1 Announce Type: new Abstract: Deploying state-of-the-art deep neural networks (DNNs) at the wireless edge is severely bottlenecked by the strict energy and resource constraints of mobile devices.
arXiv:2607. 08013v1 Announce Type: new Abstract: Federated Learning (FL) empowers multiple clients to collaboratively learn a model, enlarging the training data of each client for high accuracy while protecting data privacy.
arXiv:2608. 09250v1 Announce Type: new Abstract: Federated learning (FL) must serve devices with varying computational capabilities.
arXiv:2608. 01426v1 Announce Type: new Abstract: Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability.
arXiv:2606. 09869v1 Announce Type: cross Abstract: Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resource constrained devices while reducing overall training cost.
arXiv:2506. 18748v2 Announce Type: replace-cross Abstract: We consider resource allocation problems in multi-user wireless networks, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users.
arXiv:2607. 25835v1 Announce Type: new Abstract: Distributed constraint optimization problems (DCOPs) provide a popular framework for distributed decision making under limited communication, but many real-world instances are too large to solve monolithically.
arXiv:2606. 06687v1 Announce Type: new Abstract: We investigate cluster formation, involving the number and composition of clusters, in decentralized federated learning (FL) with heterogeneous machine learning (ML) optimizers.
arXiv:2608. 06563v1 Announce Type: new Abstract: Machine learning and optimization have advanced together, with practical demands motivating new theory and theoretical breakthroughs enabling new applications.
arXiv:2408. 05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL).
arXiv:2603. 18540v2 Announce Type: replace Abstract: The increasing complexity of neural networks poses significant challenges for democratizing federated learning (FL) on resource-constrained edge devices.