13 citations · 37 across the 8 of their papers we have counts for
5 papers · 1 filter
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning
Matias Mendieta, Taojiannan Yang, Pu Wang +3
Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices). However, the data distributi…
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge
Pinyarash Pinyoanuntapong, Prabhu Janakaraj, Ravikumar Balakrishnan +3
Federated learning (FL) is a distributed machine learning technology for next-generation AI systems that allows a number of workers, i.e., edge devices, collaboratively learn a sha…
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing
Pinyarash Pinyoanuntapong, Tagore Pothuneedi, Ravikumar Balakrishnan +3
Federated Learning (FL) over wireless multi-hop edge computing networks, i.e., multi-hop FL, is a cost-effective distributed on-device deep learning paradigm. This paper presents F…
A Novel Attribute Reconstruction Attack in Federated Learning
Lingjuan Lyu, Chen Chen
Federated learning (FL) emerged as a promising learning paradigm to enable a multitude of participants to construct a joint ML model without exposing their private training data. E…
Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy
Chen Chen, Kezhi Kong, Peihong Yu +3
Randomized smoothing (RS) is an effective and scalable technique for constructing neural network classifiers that are certifiably robust to adversarial perturbations. Most RS works…