Privacy-Preserving Federated Learning: Foundations andAlgorithmic Directions

Title

Privacy-Preserving Federated Learning: Foundations andAlgorithmic Directions

Creator

Namitha, T.N.; Raghavendra, S.; Vinith, R.

Description

Federated Learning (FL) stands at the forefront of decentralized machine learning, revolutionizing collaborative model training among distributed devices while maintaining stringent privacy standards. FL requires multiple algorithms to handle issues with model initialization, synchronization, and convergence in remote environments. This paper comprehensively examines FL algorithms, focusing on pivotal techniques such as client-side training, server-side aggregation, and FedAvg. Detailed analysis elucidates these algorithms intricate workings, showcasing how they harmonize the aggregation of local model updates with global parameter refinement, thereby striking a delicate equilibrium between privacy preservation and model accuracy. The foundations of FL and the specifics of its sophisticated algorithms are covered in this study. By providing researchers with a roadmap for delving into FL algorithm development, this paper catalyzes unlocking new avenues of innovation and advancing the frontiers of privacy-preserving machine learning. For experimental learning, the federated learning implementation is carried out using the Flower framework on the well-known iris flower classification problem, with performance metrics thoroughly evaluated. Moreover, this paper represents, to our knowledge, the first work that extends the algorithmic directions presented in a review paper with detailed implementation on a sample problem, further encouraging exploration of various algorithms in FL implementation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Networks and Systems;Volume;1333 LNNS;pp.309-319

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Algorithms; Collaborative learning; Federated learning; Flower framework; Iris flower classification; Privacy preservation

Coverage

Namitha T.N., Department of CSE, CHRIST (Deemed to be University), Bengaluru, India; Raghavendra S., Department of CSE, CHRIST (Deemed to be University), Bengaluru, India; Vinith R., School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981964535-0;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-4536-7_23

https://www.scopus.com/pages/publications/105018801087?origin=resultslist