Summary of Fedmef: Towards Memory-efficient Federated Dynamic Pruning, by Hong Huang et al.
FedMef: Towards Memory-efficient Federated Dynamic Pruningby Hong Huang, Weiming Zhuang, Chen Chen, Lingjuan LyuFirst submitted…
FedMef: Towards Memory-efficient Federated Dynamic Pruningby Hong Huang, Weiming Zhuang, Chen Chen, Lingjuan LyuFirst submitted…
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central Serverby…
Text-Enhanced Data-free Approach for Federated Class-Incremental Learningby Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi,…
Advancing IIoT with Over-the-Air Federated Learning: The Role of Iterative Magnitude Pruningby Fazal Muhammad Ali…
FedNMUT – Federated Noisy Model Update Tracking Convergence Analysisby Vishnu Pandi Chellapandi, Antesh Upadhyay, Abolfazl…
Byzantine-resilient Federated Learning With Adaptivity to Data Heterogeneityby Shiyuan Zuo, Xingrun Yan, Rongfei Fan, Han…
Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharingby Ehsan Lari, Reza Arablouei,…
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networksby Zheng Lin, Guanqiao Qu, Wei Wei,…
FedFisher: Leveraging Fisher Information for One-Shot Federated Learningby Divyansh Jhunjhunwala, Shiqiang Wang, Gauri JoshiFirst submitted…
Improving LoRA in Privacy-preserving Federated Learningby Youbang Sun, Zitao Li, Yaliang Li, Bolin DingFirst submitted…