FedDistill (FD)— Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data 2018 FML— Federated Mutual Learning 2020 FedKD— Communication-efficient federated learning via knowledge distillation Nature Communications 2022 FedProto— FedProto: Fe...
Federated Learning Library: https://fedml.ai. Contribute to kyrie-23/Awesome-Federated-Learning development by creating an account on GitHub.
This GitHub repository contains an updated list of Federated Learning papers as of December 19, 2024. The resources are collected from various sources, including arXiv, NeurIPS, ICML, ICLR, ACL, EMNLP, AAAI, IJCAI, KDD, CVPR, ICCV, ECCV, NIPS, IEEE, ACM, Springer, ScienceDirect, Wiley, ...
For Backdoor Learning papers, please visit the Backdoor Learning Repository. If you find this useful, please give us a star or support me by buying me a coffee. Due to GitHub repository limitations, we only include papers that provide accompanying code and sorted by the publish date. If you...
2020 Researcher: Tao Lin, ZJU, EPFL https://tlin-tao-lin.github.io/index.html Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning. 2020 Towards Flexible Device Participation in Federated Learning for Non-IID Data. 2020 Keywords: inactive or return incomplete updates ...
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FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization Shanghai Jiao Tong University TabLeak: Tabular Data Leakage in Federated Learning ETH Zurich Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning ...
Federated learning implemented by Pytorch. Contribute to Ereebay/Federated-Learning development by creating an account on GitHub.
server利用server上的mutual cross-entropy和client上传的mutual cross-entropy计算每个client的模型权重,并用这个权重来加权平均所有client的model。 Experiment Base model: CNN Dataset: USC-HAD: 用于human activity recognition PD-Tremor:帕金森病人的颤抖数据 Baseline: Base: 只用server上benchmark数据训练得到的model ...
微众银行 (WebBank) 进一步提出了联邦迁移学习(Federated Transfer Learning,FTL)[1,6]。FTL 通过应用同态加密(Homomorphic Encryption)和多项式近似代替差分隐私(Polynomial Approximation instead of Differential Privacy)的方法,为特定行业提供了一种更安全、更可靠的方法。与此同时,基于迁移学习的的特性,FTL 的参与方可...