Wireless network intelligence at the edge
J Park, S Samarakoon, M Bennis… - Proceedings of the …, 2019 - ieeexplore.ieee.org
Fueled by the availability of more data and computing power, recent breakthroughs in cloud-
based machine learning (ML) have transformed every aspect of our lives from face …
based machine learning (ML) have transformed every aspect of our lives from face …
Wireless edge computing with latency and reliability guarantees
Edge computing is an emerging concept based on distributed computing, storage, and
control services closer to end network nodes. Edge computing lies at the heart of the fifth …
control services closer to end network nodes. Edge computing lies at the heart of the fifth …
TKAGFL: a federated communication framework under data heterogeneity
Federated learning still faces many problems from research to technology implementation
and the most critical problem is that the communication efficiency is not high. Therefore, the …
and the most critical problem is that the communication efficiency is not high. Therefore, the …
The best of both worlds: Accurate global and personalized models through federated learning with data-free hyper-knowledge distillation
Heterogeneity of data distributed across clients limits the performance of global models
trained through federated learning, especially in the settings with highly imbalanced class …
trained through federated learning, especially in the settings with highly imbalanced class …
A payload optimization method for federated recommender systems
In this study, we introduce the payload optimization method for federated recommender
systems (FRS). In federated learning (FL), the global model payload that is moved between …
systems (FRS). In federated learning (FL), the global model payload that is moved between …
Improving Communication Efficiency of Federated Distillation via Accumulating Local Updates
As an emerging federated learning paradigm, federated distillation enables communication-
efficient model training by transmitting only small-scale knowledge during the learning …
efficient model training by transmitting only small-scale knowledge during the learning …
Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors
Conventional Federated Learning (FL) involves collaborative training of a global model
while maintaining user data privacy. One of its branches, decentralized FL, is a serverless …
while maintaining user data privacy. One of its branches, decentralized FL, is a serverless …