Fedl2p: Federated learning to personalize
Federated learning (FL) research has made progress in developing algorithms for
distributed learning of global models, as well as algorithms for local personalization of those …
distributed learning of global models, as well as algorithms for local personalization of those …
Connecting low-loss subspace for personalized federated learning
Due to the curse of statistical heterogeneity across clients, adopting a personalized
federated learning method has become an essential choice for the successful deployment of …
federated learning method has become an essential choice for the successful deployment of …
Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
A Fallah, A Mokhtari… - Advances in neural …, 2020 - proceedings.neurips.cc
Abstract In Federated Learning, we aim to train models across multiple computing units
(users), while users can only communicate with a common central server, without …
(users), while users can only communicate with a common central server, without …
Personalized federated learning through local memorization
Federated learning allows clients to collaboratively learn statistical models while keeping
their data local. Federated learning was originally used to train a unique global model to be …
their data local. Federated learning was originally used to train a unique global model to be …
Flow: per-instance personalized federated learning
K Panchal, S Choudhary, N Parikh… - Advances in Neural …, 2024 - proceedings.neurips.cc
Federated learning (FL) suffers from data heterogeneity, where the diverse data distributions
across clients make it challenging to train a single global model effectively. Existing …
across clients make it challenging to train a single global model effectively. Existing …
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 …
Exploring personalization via federated representation Learning on non-IID data
Federated Learning (FL) can learn a global model across decentralized data over different
clients. However, it is susceptible to statistical heterogeneity of client-specific data. Clients …
clients. However, it is susceptible to statistical heterogeneity of client-specific data. Clients …
Personalized federated learning with first order model optimization
While federated learning traditionally aims to train a single global model across
decentralized local datasets, one model may not always be ideal for all participating clients …
decentralized local datasets, one model may not always be ideal for all participating clients …
Personalized federated learning for heterogeneous clients with clustered knowledge transfer
Personalized federated learning (FL) aims to train model (s) that can perform well for
individual clients that are highly data and system heterogeneous. Most work in personalized …
individual clients that are highly data and system heterogeneous. Most work in personalized …
Improving federated learning personalization via model agnostic meta learning
Federated Learning (FL) refers to learning a high quality global model based on
decentralized data storage, without ever copying the raw data. A natural scenario arises with …
decentralized data storage, without ever copying the raw data. A natural scenario arises with …