Combining federated learning and edge computing toward ubiquitous intelligence in 6G network: Challenges, recent advances, and future directions

Q Duan, J Huang, S Hu, R Deng… - … Surveys & Tutorials, 2023‏ - ieeexplore.ieee.org
Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

[HTML][HTML] Federated learning for edge computing: A survey

A Brecko, E Kajati, J Koziorek, I Zolotova - Applied Sciences, 2022‏ - mdpi.com
New technologies bring opportunities to deploy AI and machine learning to the edge of the
network, allowing edge devices to train simple models that can then be deployed in practice …

Federated learning for generalization, robustness, fairness: A survey and benchmark

W Huang, M Ye, Z Shi, G Wan, H Li… - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach

A Fallah, A Mokhtari… - Advances in neural …, 2020‏ - proceedings.neurips.cc
Abstract In Federated Learning, we aim to train models across multiple computing units
(users), while users can only communicate with a common central server, without …

Personalized cross-silo federated learning on non-iid data

Y Huang, L Chu, Z Zhou, L Wang, J Liu, J Pei… - Proceedings of the …, 2021‏ - ojs.aaai.org
Non-IID data present a tough challenge for federated learning. In this paper, we explore a
novel idea of facilitating pairwise collaborations between clients with similar data. We …