6 accepted papers
Federated optimization under data heterogeneity presents a significant challenge, often leading to suboptimal model performance. While numerous methods aim to replicate the ideal performance of centralized training, they frequently fall short in highly heterogeneous settings. In this paper, we intro
The integration of Large Language Models (LLMs) into clinical applications presents transformative potential but is undermined by the critical risk of hallucination, the generation of plausible but factually incorrect information. Such failures pose a direct threat to patient safety and the integrit
This paper approaches the fundamental challenge of accelerating the inherently autoregressive nature of gradient descent (GD) like SGD and Adam through a dynamic system perspective. Specifically, we introduce a unified framework that recasts the autoregressive GD process as solving a system of trian…
Federated learning (FL) has emerged as a promising paradigm for adapting large language models (LLMs) to distributed data. To mitigate the high communication and memory overhead, parameter efficient techniques such as Low Rank Adaptation (LoRA) are widely adopted. However, practical deployments ofte
This paper explores the challenge of accelerating the sequential inference process of Diffusion Probabilistic Models (DPMs). We tackle this critical issue from a dynamic systems perspective, in which the inherent sequential nature is transformed into a parallel sampling process. Specifically, we pro…
Federated learning (FL) enables multiple clients to collaboratively train an accurate global model while protecting clients' data privacy. However, FL is susceptible to Byzantine attacks from malicious participants. Although the problem has gained significant attention, existing defenses have severa…