Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method

B Pan, W Huang, Y Shi - Advances in Neural Information …, 2025 - proceedings.neurips.cc
Integrating pretrained vision-language foundation models like CLIP into federated learning
has attracted significant attention for enhancing generalization across diverse tasks …

FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning

S Kim, M Jeong, S Kim, S Cho, S Ahn… - arxiv preprint arxiv …, 2024 - arxiv.org
Federated Learning (FL) has emerged as a pivotal framework for the development of
effective global models (global FL) or personalized models (personalized FL) across clients …

Federated Learning with Efficient Local Adaptation for Realized Volatility Prediction

L Zhao, L Cai, WS Lu - Transactions on Machine Learning Research - openreview.net
Financial markets present unique challenges for Federated Learning (FL) due to fragmented
datasets, dynamic participation, and the critical need for precise and reliable predictions …

Optimal Client Training in Federated Learning with Deep Reinforcement Learning

A Murad, B Hui, WS Ku - openreview.net
Federated Learning (FL) is a distributed framework for collaborative model training over
large-scale distributed data. Centralized FL leverages a server to aggregate client models …

FedDFQ: Personalized Federated Learning Based On Data Feature Quantification

Z Chen, J Chen, Y Zheng - openreview.net
Personalized federated learning is widely used for heterogeneous data distributions across
clients. However, existing methods are difficult to measure and utilize these heterogeneities …