Zobrazeno 1 - 10
of 1 290
pro vyhledávání: '"Jiang, Xuefeng."'
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 inevitably heterogeneous, and if the datasets can be globally aggregat
Externí odkaz:
http://arxiv.org/abs/2409.12105
Autor:
Jiang, Xuefeng, Sun, Sheng, Li, Jia, Xue, Jingjing, Li, Runhan, Wu, Zhiyuan, Xu, Gang, Wang, Yuwei, Liu, Min
Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive applications without sacrificing the sensitive private information of clients. However, the data quality of client datasets can not be guaranteed since corres
Externí odkaz:
http://arxiv.org/abs/2408.04301
Autor:
Jiang, Xuefeng, Wang, Fangyuan, Zheng, Rongzhang, Liu, Han, Huo, Yixiong, Peng, Jinzhang, Tian, Lu, Barsoum, Emad
Precise localization is of great importance for autonomous parking task since it provides service for the downstream planning and control modules, which significantly affects the system performance. For parking scenarios, dynamic lighting, sparse tex
Externí odkaz:
http://arxiv.org/abs/2407.05017
Federated Learning (FL) enables collaborative model training among participants while guaranteeing the privacy of raw data. Mainstream FL methodologies overlook the dynamic nature of real-world data, particularly its tendency to grow in volume and di
Externí odkaz:
http://arxiv.org/abs/2401.00622
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
Autor:
Wang, Yuwei, Li, Runhan, Tan, Hao, Jiang, Xuefeng, Sun, Sheng, Liu, Min, Gao, Bo, Wu, Zhiyuan
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 realistic FL settings, as it causes significant performance deterioratio
Externí odkaz:
http://arxiv.org/abs/2311.08202
Autor:
Wu, Zhiyuan, Sun, Sheng, Wang, Yuwei, Liu, Min, Xu, Ke, Wang, Wen, Jiang, Xuefeng, Gao, Bo, Lu, Jinda
Edge Intelligence (EI) allows Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data sources. To protect data privacy and unify data silos among end device
Externí odkaz:
http://arxiv.org/abs/2308.07816
Publikováno v:
2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Federated Learning (FL) emerges as a distributed machine learning paradigm without end-user data transmission, effectively avoiding privacy leakage. Participating devices in FL are usually bandwidth-constrained, and the uplink is much slower than the
Externí odkaz:
http://arxiv.org/abs/2307.07172
Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning. Existing research, relying on the assumption of class-balanced global data, might be incapable to model complicated labe
Externí odkaz:
http://arxiv.org/abs/2305.05230