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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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FedMSA: A Model Selection and Adaptation System for Federated Learning.

Rui Sun¹, Yinhao Li¹, Tejal Shah¹

¹School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.

Sensors (Basel, Switzerland)

|October 14, 2022

Summary

This summary is machine-generated.

Federated Learning (FL) faces challenges in model selection for diverse devices. FedMSA offers a hardware-aware system for efficient, automated model adaptation, improving FL performance on heterogeneous hardware.

Keywords:

device adaptation distributed system federated learning model adaptation model selection orchestration

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Area of Science:

Artificial Intelligence
Machine Learning
Distributed Systems

Background:

Federated Learning (FL) allows collaborative model training without data sharing.
Large-scale FL applications with heterogeneous devices face challenges in model selection and adaptation.
Current FL systems struggle to balance model performance and training efficiency across diverse hardware.

Purpose of the Study:

To propose a novel system, FedMSA, for automated model selection and adaptation in Federated Learning.
To develop a hardware-aware algorithm for optimizing model training efficiency and performance.
To enable scalable deployment of FL tasks across heterogeneous devices.

Main Methods:

Introduced FedMSA, a system incorporating a hardware-aware model selection algorithm.
Implemented dynamic model adaptation for automated FL task building and deployment.
Evaluated FedMSA on benchmark and real-world datasets using devices like Raspberry Pi and Jetson Nano.

Main Results:

FedMSA demonstrated effectiveness in model selection and adaptation for Federated Learning.
The hardware-aware algorithm successfully balanced training efficiency and model performance.
Automated deployment and adaptation were achieved at scale across different hardware.

Conclusions:

FedMSA provides an effective solution for model selection and adaptation in large-scale, heterogeneous Federated Learning.
The system enhances FL deployment efficiency and performance on diverse edge devices.
FedMSA facilitates the development and deployment of user-centric FL applications.