22 citations · 48 across the 18 of their papers we have counts for
4 papers · 1 filter
Distributed Bilevel Optimization with Dual Pruning for Resource-limited Clients
Mingyi Li, Xiao Zhang, Ruisheng Zheng +4
With the development of large-scale models, traditional distributed bilevel optimization algorithms cannot be applied directly in low-resource clients. The key reason lies in the e…
FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality
Biwei Yan, Hongliang Zhang, Minghui Xu +2
Federated learning is a powerful technique that enables collaborative learning among different clients. Prototype-based federated learning is a specific approach that improves the…
Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity
Kun Li, Shengling Wang, Hongwei Shi +2
Spatial crowdsourcing (SC) engages large worker pools for location-based tasks, attracting growing research interest. However, prior SC task allocation approaches exhibit limitatio…
Byzantine-Resilient Federated Learning at Edge
Youming Tao, Sijia Cui, Wenlu Xu +4
Both Byzantine resilience and communication efficiency have attracted tremendous attention recently for their significance in edge federated learning. However, most existing algori…