FedAgg: Adaptive Federated Learning with Aggregated Gradients

28 March 2023

Papers citing "FedAgg: Adaptive Federated Learning with Aggregated Gradients"

6 / 6 papers shown

Title
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout Chen Dun Mirian Hipolito Garcia C. Jermaine Dimitrios Dimitriadis Anastasios Kyrillidis 56 20 0 28 Oct 2022
Asynchronous Federated Learning on Heterogeneous Devices: A Survey Chenhao Xu Youyang Qu Yong Xiang Longxiang Gao FedML 93 241 0 09 Sep 2021
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients A. Mitra Rayana H. Jaafar George J. Pappas Hamed Hassani FedML 55 157 0 14 Feb 2021
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations Saima Sharmin Nitin Rathi Priyadarshini Panda Kaushik Roy AAML 111 85 0 23 Mar 2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 162 760 0 28 Sep 2019
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 249 36,362 0 25 Aug 2016