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Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method
Integrating pretrained vision-language foundation models like CLIP into federated learning
has attracted significant attention for enhancing generalization across diverse tasks …
has attracted significant attention for enhancing generalization across diverse tasks …
FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning
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 …
effective global models (global FL) or personalized models (personalized FL) across clients …
Federated Learning with Efficient Local Adaptation for Realized Volatility Prediction
Financial markets present unique challenges for Federated Learning (FL) due to fragmented
datasets, dynamic participation, and the critical need for precise and reliable predictions …
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 …
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 …
clients. However, existing methods are difficult to measure and utilize these heterogeneities …