arXiv:2207.09158
공식 코드 · Papers with Code
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation
원문 초록
This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased representation from decentralized and heterogeneous local data. It employs a two-sided knowledge distillation with contrastive learning as a core component, allowing the federated system to function without requiring clients to share any data features. Furthermore, its adaptable architecture can be used as an add-on module for existing unsupervised algorithms in federated settings. Experiments show that our model improves performance significantly (1.58--5.52pp) on five unsupervised algorithms.
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FedSC: Federated Learning with Semantic-Aware Collaboration
FedBKD: Distilled Federated Learning to Embrace Gerneralization and Personalization on Non-IID Data
Tackling Data Heterogeneity in Federated Learning through Knowledge Distillation with Inequitable Aggregation
Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning
SFedKD: Sequential Federated Learning with Discrepancy-Aware Multi-Teacher Knowledge Distillation
Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift
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