WebJun 4, 2024 · Client contribution evaluation, also known as data valuation, is a crucial approach in federated learning (FL) for client selection and incentive allocation. However, due to restrictions of accessibility of raw data, only limited information such as local weights and local data size of each client is open for quantifying the client contribution. Web本文介绍南京大学 Websoft 组在 WWW 2024 中提出的一种异构联邦知识图谱表示学习与遗忘框架。. 论文: Xiangrong Zhu, Guangyao Li, Wei Hu. Federated Knowledge Graph Embedding Learning and Unlearning. In WWW, 2024. [][背景. 作为一种创新性的分布式机器学习范式，联邦学习可以在不共享本地数据的情况下联合多个客户端协同训练 ...

VeriFi: Towards Verifiable Federated Unlearning DeepAI

WebSuch a machine unlearning problem becomes more challenging in the context of federated learning, where clients collaborate to train a global model with their private data. ... Over a variety of datasets and tasks, we have shown clear evidence that Knot outperformed the state-of-the-art federated unlearning mechanisms by up to 85% in the context ... huationg asia pte. ltd

[2012.13891] Federated Unlearning - arXiv.org

Web集中式泰勒展开逆推模型遗忘. Contribute to yujingda/taylor_exp_machine_unlearn development by creating an account on GitHub. WebMachine Unlearning of Federated Clusters. Federated Neural Bandits. FedFA: Federated Feature Augmentation. Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. Better Generative Replay for Continual Federated Learning. Federated Learning from Small Datasets. Federated Nearest Neighbor … WebSynonyms for UNLEARNING: forgetting, losing, missing, disremembering, ignoring, misremembering, blanking, neglecting; Antonyms of UNLEARNING: remembering ... huatran官网