arXiv:2502.10239v2 Announce Type: replace-cross
Abstract: Federated fine-tuning offers a promising approach for tuning Large Language Models (LLMs) on edge devices while preserving data privacy. However, fine-tuning these models on edge devices remains challenging due to high memory, communication, and computational demands. Zero-order optimization with task alignment provides a potential solution, enabling fine-tuning with inference-level memory requirements but requires a longer convergence time. In this paper, we propose acMETHOD that divides the network into two blocks, applying a different number of perturbations per block in a computationally effective way, achieving faster convergence. Our evaluation shows a $1.6-3times$ reduction in computation overhead compared to zero-order state of the art techniques in federated learning.
The Impact of Patient-Generated Health Data From Mobile Health Technologies on Health Care Management and Clinical Decision-Making: Narrative Scoping Review
Background: Long-term health conditions and multimorbidity are increasing globally placing an unsustainable pressure on healthcare systems. Mobile health technologies, or mHealth, enable the collection of