User-Oriented Multi-Task Federated Deep Learning for Mobile Edge Computing

07/17/2020
by   Jed Mills, et al.
0

Federated Learning (FL) is a recent approach for collaboratively training Machine Learning models on mobile edge devices, without private user data leaving the devices. The popular FL algorithm, Federated Averaging (FedAvg), suffers from poor convergence speed given non-iid user data. Furthermore, most existing work on FedAvg measures central-model accuracy, but in many cases, such as user content-recommendation, improving individual User model Accuracy (UA) is the real objective. To address these issues, we propose a Multi-Task Federated Learning (MTFL) system, which converges faster than FedAvg by using distributed Adam optimization (FedAdam), and benefits UA by introducing personal, non-federated 'patch' Batch-Normalization (BN) layers into the model. Testing FedAdam on the MNIST and CIFAR10 datasets show that it converges faster (up to 5x) than FedAvg in non-iid scenarios, and experiments using MTFL on the CIFAR10 dataset show that MTFL significantly improves average UA over FedAvg, by up to 54 MTFL model during inference, and give evidence that MTFL strikes a better balance between regularization and convergence in FL. Finally, we test the MTFL system on a mobile edge computing testbed, showing that MTFL's convergence and UA benefits outweigh its overhead.

READ FULL TEXT

page 3

page 5

research
05/16/2019

Edge-Assisted Hierarchical Federated Learning with Non-IID Data

Federated Learning (FL) is capable of leveraging massively distributed p...
research
03/12/2021

SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems

Federated learning (FL) utilizes edge computing devices to collaborative...
research
02/17/2022

FLAME: Federated Learning Across Multi-device Environments

Federated Learning (FL) enables distributed training of machine learning...
research
07/02/2019

Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications

Federated learning (FL) is a distributed deep learning method which enab...
research
08/24/2021

Data-Free Evaluation of User Contributions in Federated Learning

Federated learning (FL) trains a machine learning model on mobile device...
research
04/07/2022

Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation

The widespread application of artificial intelligence in health research...
research
08/14/2023

FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing

As a typical entity of MEC (Mobile Edge Computing), 5G CPE (Customer Pre...

Please sign up or login with your details

Forgot password? Click here to reset