Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point

03/26/2022
by   Bhargav Ganguly, et al.
0

We propose cooperative edge-assisted dynamic federated learning (CE-FL). CE-FL introduces a distributed machine learning (ML) architecture, where data collection is carried out at the end devices, while the model training is conducted cooperatively at the end devices and the edge servers, enabled via data offloading from the end devices to the edge servers through base stations. CE-FL also introduces floating aggregation point, where the local models generated at the devices and the servers are aggregated at an edge server, which varies from one model training round to another to cope with the network evolution in terms of data distribution and users' mobility. CE-FL considers the heterogeneity of network elements in terms of communication/computation models and the proximity to one another. CE-FL further presumes a dynamic environment with online variation of data at the network devices which causes a drift at the ML model performance. We model the processes taken during CE-FL, and conduct analytical convergence analysis of its ML model training. We then formulate network-aware CE-FL which aims to adaptively optimize all the network elements via tuning their contribution to the learning process, which turns out to be a non-convex mixed integer problem. Motivated by the large scale of the system, we propose a distributed optimization solver to break down the computation of the solution across the network elements. We finally demonstrate the effectiveness of our framework with the data collected from a real-world testbed.

READ FULL TEXT
research
05/16/2019

Edge-Assisted Hierarchical Federated Learning with Non-IID Data

Federated Learning (FL) is capable of leveraging massively distributed p...
research
05/31/2023

An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneity

Nowadays, billions of phones, IoT and edge devices around the world gene...
research
11/02/2021

FedFly: Towards Migration in Edge-based Distributed Federated Learning

Federated learning (FL) is a privacy-preserving distributed machine lear...
research
01/26/2023

Time-sensitive Learning for Heterogeneous Federated Edge Intelligence

Real-time machine learning has recently attracted significant interest d...
research
12/21/2021

On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edge

Edge computing has revolutionized the world of mobile and wireless netwo...
research
08/20/2023

Arena: A Learning-based Synchronization Scheme for Hierarchical Federated Learning–Technical Report

Federated learning (FL) enables collaborative model training among distr...
research
07/05/2021

Towards Node Liability in Federated Learning: Computational Cost and Network Overhead

Many machine learning (ML) techniques suffer from the drawback that thei...

Please sign up or login with your details

Forgot password? Click here to reset