Fusion of global and local knowledge for personalized federated learning
Personalized federated learning, as a variant of federated learning, trains customized
models for clients using their heterogeneously distributed data. However, it is still …
models for clients using their heterogeneously distributed data. However, it is still …
ZooPFL: Exploring black-box foundation models for personalized federated learning
When personalized federated learning (FL) meets large foundation models, new challenges
arise from various limitations in resources. In addition to typical limitations such as data …
arise from various limitations in resources. In addition to typical limitations such as data …
Personalized federated learning with contextualized generalization
The prevalent personalized federated learning (PFL) usually pursues a trade-off between
personalization and generalization by maintaining a shared global model to guide the …
personalization and generalization by maintaining a shared global model to guide the …
Efficient personalized federated learning via sparse model-adaptation
Federated Learning (FL) aims to train machine learning models for multiple clients without
sharing their own private data. Due to the heterogeneity of clients' local data distribution …
sharing their own private data. Due to the heterogeneity of clients' local data distribution …
FedCR: Personalized federated learning based on across-client common representation with conditional mutual information regularization
In personalized federated learning (PFL), multiple clients train customized models to fulfill
their personal objectives, which, however, are prone to overfitting to local data due to the …
their personal objectives, which, however, are prone to overfitting to local data due to the …
Spectral co-distillation for personalized federated learning
Personalized federated learning (PFL) has been widely investigated to address the
challenge of data heterogeneity, especially when a single generic model is inadequate in …
challenge of data heterogeneity, especially when a single generic model is inadequate in …
Fed-CO: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning
Federated Learning (FL) has emerged as a promising distributed learning paradigm that
enables multiple clients to learn a global model collaboratively without sharing their private …
enables multiple clients to learn a global model collaboratively without sharing their private …
Efficient federated learning via local adaptive amended optimizer with linear speedup
Adaptive optimization has achieved notable success for distributed learning while extending
adaptive optimizer to federated Learning (FL) suffers from severe inefficiency, including (i) …
adaptive optimizer to federated Learning (FL) suffers from severe inefficiency, including (i) …
Fedpara: Low-rank hadamard product for communication-efficient federated learning
In this work, we propose a communication-efficient parameterization, FedPara, for federated
learning (FL) to overcome the burdens on frequent model uploads and downloads. Our …
learning (FL) to overcome the burdens on frequent model uploads and downloads. Our …