Intelligent Bilateral Client Selection in Federated Learning Using Game Theory

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. To overcome this problem, we present in this paper Fed Mint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves designing (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the new connected IoT devices. Based on our simulation findings, our strategy surpasses the Vanilla F selection approach in terms of maximizing both the revenues of the client devices and accuracy of the global federated learning model.

روابط وملفات

التعريف والنوع

رقم الوثيقة

0c15eecb-ad2e-4827-b5ed-dc4e798e3d5b

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

2212494_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":" Azzam Mourad","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Osama Mohammad Wehbi","title_ar":"اعداد","title_en":"Preparation"}]

اللغة

English

المصدر والدورية

اسم المصدر

Intelligent Bilateral Client Selection in Federated Learning Using Game Theory

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Using Game Theory

إشراف وإعداد

الإشراف

Azzam Mourad

الإعداد

Osama Mohammad Wehbi

الاقتباسات الببليوغرافية