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Heterogeneous federated learning: State-of-the-art and research challenges
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …
scale industrial applications. Existing FL works mainly focus on model homogeneous …
Privacy and fairness in federated learning: On the perspective of tradeoff
Federated learning (FL) has been a hot topic in recent years. Ever since it was introduced,
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
researchers have endeavored to devise FL systems that protect privacy or ensure fair …
Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models
Public large-scale text-to-image diffusion models, such as Stable Diffusion, have gained
significant attention from the community. These models can be easily customized for new …
significant attention from the community. These models can be easily customized for new …
Deep long-tailed learning: A survey
Deep long-tailed learning, one of the most challenging problems in visual recognition, aims
to train well-performing deep models from a large number of images that follow a long-tailed …
to train well-performing deep models from a large number of images that follow a long-tailed …
Federated learning from pre-trained models: A contrastive learning approach
Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …
learn collaboratively without sharing their private data. However, excessive computation and …
Fedfed: Feature distillation against data heterogeneity in federated learning
Federated learning (FL) typically faces data heterogeneity, ie, distribution shifting among
clients. Sharing clients' information has shown great potentiality in mitigating data …
clients. Sharing clients' information has shown great potentiality in mitigating data …
Federated learning for generalization, robustness, fairness: A survey and benchmark
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …
collaboration among different parties. Recently, with the popularity of federated learning, an …
Rethinking federated learning with domain shift: A prototype view
Federated learning shows a bright promise as a privacy-preserving collaborative learning
technique. However, prevalent solutions mainly focus on all private data sampled from the …
technique. However, prevalent solutions mainly focus on all private data sampled from the …
Feddisco: Federated learning with discrepancy-aware collaboration
This work considers the category distribution heterogeneity in federated learning. This issue
is due to biased labeling preferences at multiple clients and is a typical setting of data …
is due to biased labeling preferences at multiple clients and is a typical setting of data …
Fedbabu: Towards enhanced representation for federated image classification
Federated learning has evolved to improve a single global model under data heterogeneity
(as a curse) or to develop multiple personalized models using data heterogeneity (as a …
(as a curse) or to develop multiple personalized models using data heterogeneity (as a …