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FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillation

Autor: Pan, Quyang, Sun, Sheng, Wu, Zhiyuan, Wang, Yuwei, Liu, Min, Gao, Bo, Wang, Jingyuan

Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite its advantages, practical FEL deployment faces significant challenges

Externí odkaz: http://arxiv.org/abs/2405.13378

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Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration

Autor: Wu, Zhiyuan, Sun, Sheng, Wang, Yuwei, Liu, Min, Gao, Bo, Pan, Quyang, He, Tianliu, Jiang, Xuefeng

Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices without compromising their privacy. As computing tasks are increasingly performed by a combination of cloud, edge, and end devices, FL can benefit from this

Externí odkaz: http://arxiv.org/abs/2312.11489

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FedICT: Federated Multi-task Distillation for Multi-access Edge Computing

Autor: Wu, Zhiyuan, Sun, Sheng, Wang, Yuwei, Liu, Min, Pan, Quyang, Jiang, Xuefeng, Gao, Bo

The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning in Multi-access Edge Computing (MEC). Diverse user behaviors call for personalized services with

Externí odkaz: http://arxiv.org/abs/2301.00389

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Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation

Autor: Wu, Zhiyuan, Sun, Sheng, Wang, Yuwei, Liu, Min, Pan, Quyang, Zhang, Junbo, Li, Zeju, Liu, Qingxiang

Federated learning (FL) is a privacy-preserving machine learning paradigm in which the server periodically aggregates local model parameters from clients without assembling their private data. Constrained communication and personalization requirement

Externí odkaz: http://arxiv.org/abs/2204.07028

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