1.2k citations · 9.5k across the 261 of their papers we have counts for
11 papers · 2 filters
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning
Stefano Savazzi, Vittorio Rampa, Sanaz Kianoush +1
Recent advances in Federated Learning (FL) have paved the way towards the design of novel strategies for solving multiple learning tasks simultaneously, by leveraging cooperation a…
Federated Learning and Meta Learning: Approaches, Applications, and Directions
Xiaonan Liu, Yansha Deng, Arumugam Nallanathan +1
Over the past few years, significant advancements have been made in the field of machine learning (ML) to address resource management, interference management, autonomy, and decisi…
DR-DSGD: A Distributionally Robust Decentralized Learning Algorithm over Graphs
Chaouki Ben Issaid, Anis Elgabli, Mehdi Bennis
In this paper, we propose to solve a regularized distributionally robust learning problem in the decentralized setting, taking into account the data distribution shift. By adding a…
Slimmable Quantum Federated Learning
Won Joon Yun, Jae Pyoung Kim, Soyi Jung +3
Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL). In contrast to the ex…
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning
Sihun Baek, Jihong Park, Praneeth Vepakomma +3
This article seeks for a distributed learning solution for the visual transformer (ViT) architectures. Compared to convolutional neural network (CNN) architectures, ViTs often have…
An Energy and Carbon Footprint Analysis of Distributed and Federated Learning
Stefano Savazzi, Vittorio Rampa, Sanaz Kianoush +1
Classical and centralized Artificial Intelligence (AI) methods require moving data from producers (sensors, machines) to energy hungry data centers, raising environmental concerns…
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
Anis Elgabli, Chaouki Ben Issaid, Amrit S. Bedi +3
Newton-type methods are popular in federated learning due to their fast convergence. Still, they suffer from two main issues, namely: low communication efficiency and low privacy d…
Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach
Zhiheng Wang, Yanxiang Jiang, Fu-Chun Zheng +2
In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. Based on clustered federated learning, we propose a novel mobility-a…
Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach
Lingling Zhang, Yanxiang Jiang, Fu-Chun Zheng +2
The fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs). Due to…
Time-triggered Federated Learning over Wireless Networks
Xiaokang Zhou, Yansha Deng, Huiyun Xia +2
The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based…
SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks
Won Joon Yun, Yunseok Kwak, Hankyul Baek +5
Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices' distributed computing capabilities. However, applying FL in practice is chal…