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arXiv Machine Learning

Communication-Efficient Personalized Federated Learning via Layer-Wise Multi-Threshold Random Sketching

The paper introduces a communication‑efficient personalized federated learning framework that uses layer‑wise multi‑threshold random sketching. By assigning each neural network layer its own set of quantization thresholds, the method adapts to layer‑specific parameter distributions and provides a finer low‑bit representation than single‑threshold one‑bit compression. This approach supports bidirectional communication with compact sketches and improves the communication‑accuracy tradeoff over existing one‑bit methods.

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arXiv Machine Learning
1d ago

FedSAP: Federated Learning with Structured Adaptive Partitioning for Multi-Domain Heterogeneous Edge Devices

FedSAP is a federated learning framework that addresses heterogeneous edge devices by using structured pruning as a budget-constrained tri-state channel allocation. It partitions model channels into a Global pool, pseudo-domain-specific Private pools, and a Dropped state, allowing broadly useful features to be shared while isolating domain-sensitive updates. Experiments on Digits and Office-Caltech datasets show FedSAP achieving higher mean global accuracy than the strongest baseline while supporting up to 80% client pruning ratios.

By Wentao Yue, Tianyou Lai, Hongji Li, Qingyu Mao, Qilei Li
efficiency
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arXiv Machine Learning
Jun 26

Quantization in Federated Learning: Methods, Challenges and Future Directions

arXiv:2606. 26822v1 Announce Type: new Abstract: Federated Learning (FL) has become a foundational paradigm for privacy-preserving distributed intelligence, yet its scalability remains fundamentally constrained by communication bottlenecks, device heterogeneity, and the challenges of training under statistically non-IID data.

By Farwa Ikram, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino
efficiencysafety
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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
llmsfine-tuningsafety
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arXiv Machine Learning
Aug 27

Differentiated Aggregation to Improve Generalization in Federated Learning

The paper proposes a new federated learning approach called FedALS that reduces communication costs by varying aggregation frequencies across model layers. It derives tighter generalization bounds for one‑round and multi‑round federated learning, linking these bounds to local updates and data heterogeneity. Based on representation‑learning insights, the authors argue that infrequent aggregation of early layers and more frequent aggregation of final layers yields more generalizable models, especially in non‑iid settings, and demonstrate the method’s effectiveness experimentally.

By Peyman Gholami, Hulya Seferoglu
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