25 citations · 67 across the 13 of their papers we have counts for
10 papers
Parameterized Knowledge Transfer for Personalized Federated Learning
Jie Zhang, Song Guo, Xiaosong Ma +3
In recent years, personalized federated learning (pFL) has attracted increasing attention for its potential in dealing with statistical heterogeneity among clients. However, the st…
SDN-based Resource Allocation in Edge and Cloud Computing Systems: An Evolutionary Stackelberg Differential Game Approach
Jun Du, Chunxiao Jiang, Abderrahim Benslimane +2
Recently, the boosting growth of computation-heavy applications raises great challenges for the Fifth Generation (5G) and future wireless networks. As responding, the hybrid edge a…
Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities
Yuben Qu, Haipeng Dai, Yan Zhuang +4
Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields. Empowering…
Optimizing Video Caching at the Edge: A Hybrid Multi-Point Process Approach
Xianzhi Zhang, Yipeng Zhou, Di Wu +4
It is always a challenging problem to deliver a huge volume of videos over the Internet. To meet the high bandwidth and stringent playback demand, one feasible solution is to cache…
Gain without Pain: Offsetting DP-injected Nosies Stealthily in Cross-device Federated Learning
Wenzhuo Yang, Yipeng Zhou, Maio Hu +4
Federated Learning (FL) is an emerging paradigm through which decentralized devices can collaboratively train a common model. However, a serious concern is the leakage of privacy f…
Scalable and Communication-efficient Decentralized Federated Edge Learning with Multi-blockchain Framework
Jiawen Kang, Zehui Xiong, Chunxiao Jiang +6
The emerging Federated Edge Learning (FEL) technique has drawn considerable attention, which not only ensures good machine learning performance but also solves "data island" proble…