63 citations · 69 across the 4 of their papers we have counts for
6 papers
FedLF: Adaptive Logit Adjustment and Feature Optimization in Federated Long-Tailed Learning
Xiuhua Lu, Peng Li, Xuefeng Jiang
Federated learning offers a paradigm to the challenge of preserving privacy in distributed machine learning. However, datasets distributed across each client in the real world are…
Federated Class-Incremental Learning with New-Class Augmented Self-Distillation
Zhiyuan Wu, Tianliu He, Sheng Sun +4
Federated Learning (FL) enables collaborative model training among participants while guaranteeing the privacy of raw data. Mainstream FL methodologies overlook the dynamic nature…
Federated Skewed Label Learning with Logits Fusion
Yuwei Wang, Runhan Li, Hao Tan +5
Federated learning (FL) aims to collaboratively train a shared model across multiple clients without transmitting their local data. Data heterogeneity is a critical challenge in re…
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout
Jingjing Xue, Min Liu, Sheng Sun +3
Federated Learning (FL) emerges as a distributed machine learning paradigm without end-user data transmission, effectively avoiding privacy leakage. Participating devices in FL are…
FedTrip: A Resource-Efficient Federated Learning Method with Triplet Regularization
Xujing Li, Min Liu, Sheng Sun +3
In the federated learning scenario, geographically distributed clients collaboratively train a global model. Data heterogeneity among clients significantly results in inconsistent…
Towards Federated Learning against Noisy Labels via Local Self-Regularization
Xuefeng Jiang, Sheng Sun, Yuwei Wang +1
Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in a privacy-preserving manner. However, since high-quality labe…