3 accepted papers
We investigate the robustness of the recently introduced partialsharing online federated learning (PSO-Fed) algorithm against model-poisoning attacks. To this end, we analyze the performance of the PSO-Fed algorithm in the presence of Byzantine clients, who may clandestinely corrupt their local mode…
Federated learning (FL) literature typically assumes that each client has a fixed amount of data, which is unrealistic in many practical applications. Some recent works introduced a framework for online FL (Online-Fed) wherein clients perform model learning on streaming data and communicate the mode…
This work proposes an efficient batch-based implementation for kernel regression on graphs (KRG) using random Fourier features (RFF) and a low-complexity online implementation. Kernel regression has proven to be an efficient learning tool in the graph signal processing framework. However, it suffers…