Fedsr: A simple and effective domain generalization method for federated learning
Federated Learning (FL) refers to the decentralized and privacy-preserving machine
learning framework in which multiple clients collaborate (with the help of a central server) to …
learning framework in which multiple clients collaborate (with the help of a central server) to …
FedFA: Federated learning with feature anchors to align features and classifiers for heterogeneous data
Federated learning allows multiple clients to collaboratively train a model without
exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant …
exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant …
PFLlib: Personalized Federated Learning Algorithm Library
Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm
that allows collaborative learning with data privacy protection, personalized FL (pFL) has …
that allows collaborative learning with data privacy protection, personalized FL (pFL) has …
Fed2: Feature-aligned federated learning
Federated learning learns from scattered data by fusing collaborative models from local
nodes. However, conventional coordinate-based model averaging by FedAvg ignored the …
nodes. However, conventional coordinate-based model averaging by FedAvg ignored the …
Shapleyfl: Robust federated learning based on shapley value
Federated Learning (FL) allows clients to form a consortium to train a global model under
the orchestration of a central server while keeping data on the local client without sharing it …
the orchestration of a central server while keeping data on the local client without sharing it …
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning
Federated Learning (FL) is a privacy-constrained decentralized machine learning paradigm
in which clients enable collaborative training without compromising private data. However …
in which clients enable collaborative training without compromising private data. However …
Reducing communication in federated learning via efficient client sampling
Federated learning (FL) ameliorates privacy concerns in settings where a central server
coordinates learning from data distributed across many clients; rather than sharing the data …
coordinates learning from data distributed across many clients; rather than sharing the data …
Rethinking data heterogeneity in federated learning: Introducing a new notion and standard benchmarks
Though successful, federated learning (FL) presents new challenges for machine learning,
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …
especially when the issue of data heterogeneity, also known as Non-IID data, arises. To …
Heterogeneous federated learning via grouped sequential-to-parallel training
Federated learning (FL) is a rapidly growing privacy preserving collaborative machine
learning paradigm. In practical FL applications, local data from each data silo reflect local …
learning paradigm. In practical FL applications, local data from each data silo reflect local …
Adaptive client clustering for efficient federated learning over non-iid and imbalanced data
Federated learning (FL) is an emerging distributed and privacy-preserving machine learning
framework. However, the performance of traditional FL methods is seriously impaired by the …
framework. However, the performance of traditional FL methods is seriously impaired by the …