A systematic review of federated learning from clients' perspective: challenges and solutions

Y Shanmugarasa, H Paik, SS Kanhere… - Artificial Intelligence …, 2023 - Springer
Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …

On privacy and personalization in cross-silo federated learning

K Liu, S Hu, SZ Wu, V Smith - Advances in neural …, 2022 - proceedings.neurips.cc
While the application of differential privacy (DP) has been well-studied in cross-device
federated learning (FL), there is a lack of work considering DP and its implications for cross …

Private adaptive optimization with side information

T Li, M Zaheer, S Reddi… - … Conference on Machine …, 2022 - proceedings.mlr.press
Adaptive optimization methods have become the default solvers for many machine learning
tasks. Unfortunately, the benefits of adaptivity may degrade when training with differential …

Private federated learning without a trusted server: Optimal algorithms for convex losses

A Lowy, M Razaviyayn - ar** machine learning models heavily relies on access to data. To build a
responsible data economy and protect data ownership, it is crucial to enable learning …

[PDF][PDF] On Privacy and Personalization in Machine Learning

KZ Liu - 2023 - ri.cmu.edu
While the application of differential privacy (DP) has been well-studied in cross-device
federated learning (FL), there is a relative lack of work considering DP and its implications …

Key Generation and Secure Coding in Communications and Private Learning

N Aldaghri - 2022 - deepblue.lib.umich.edu
The increasingly distributed nature of many current and future technologies has introduced
many challenges for devices designed for such settings. Devices operating in such …