arXiv Machine Learning August 4, 2026 By Zheng Lin, Ons Aouedi, Zihan Fang, Wei Ni, Yue Gao, Symeon Chatzinotas, Xianhao Chen

GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systems

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 10

Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems

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.

By Shuo Huai, Di Liu, Hao Kong, Xiangzhong Luo, Weichen Liu, Ravi Subramaniam, Christian Makaya, Qian Lin

benchmarks safety

arXiv Machine Learning

Jul 21

AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning

arXiv:2607. 17913v1 Announce Type: cross Abstract: Distributed Fine-Tuning (DFT) of large-scale Foundation Models (FMs) on resource-constrained edge devices is limited by local compute constraints and communication overhead.

By Bas Meuwissen, Vasileios Tsouvalas, Nirvana Meratnia

fine-tuning efficiency safety

arXiv Machine Learning

Aug 5

AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning

arXiv:2608. 03324v1 Announce Type: new Abstract: Federated learning enables collaborative model training across distributed edge devices while strictly preserving data privacy.

By Shengyang Li, Yiting Dong, Liuyang Song, Ximing Wang, Luyuan Xie, Cong Li, Qingni Shen, Zhaofei Yu

efficiency benchmarks safety

arXiv AI

3d ago

HO-FL: Hybrid-Order Federated Learning for Heterogeneous Edge Devices

arXiv:2609.39074v1 Announce Type: cross Abstract: Federated learning (FL) on memory-constrained edge devices faces a dilemma: first-order (FO) optimization (i.e., backpropagation) demands substantial...

By Qiyuan Chen, Xian Wu, Yanan Ma, Xianhao Chen

safety

arXiv Machine Learning

Sep 2

FlexP-SFT: A Flexible Aggregation-Free Framework for On-Device Personalized Split Federated Fine-Tuning of LLMs

FlexP-SFT introduces an aggregation-free framework for personalized split federated fine-tuning of large language models, eliminating the client-side aggregation step that traditionally causes communication bottlenecks and straggler issues. The method employs a layer‑flexible alignment strategy to balance personalization and generalization without global synchronization, and formulates split‑ratio selection as a resource‑aware discrete optimization problem. Experiments demonstrate that FlexP-SFT improves both accuracy and latency compared to baselines, achieving a superior resource‑accuracy trade‑off.

By Jiaxiang Geng, Tianjun Yuan, Pengchao Han, Ying Gao, Xianhao Chen, Bing Luo

llms fine-tuning safety

arXiv Machine Learning

Jun 15

Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learning

arXiv:2602. 02355v2 Announce Type: replace-cross Abstract: Hierarchical federated learning (HFL) is well suited for large-scale wireless and Internet of Things systems, where devices communicate with nearby edge servers before reaching the cloud.

By Amirreza Kazemi, Seyed Mohammad Azimi-Abarghouyi, Gabor Fodor, Carlo Fischione

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Collate: Collaborative Neural Network Learning for Latency-Critical Edge Systems

AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning

AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning

HO-FL: Hybrid-Order Federated Learning for Heterogeneous Edge Devices

FlexP-SFT: A Flexible Aggregation-Free Framework for On-Device Personalized Split Federated Fine-Tuning of LLMs

Mitigating Heterogeneity-Induced Drift in Hierarchical Sign-Based Federated Learning