Work

Trust and Resilience in Federated Learning Through Smart Contracts Enabled Decentralized Systems

Year: 2024

Type: conference-paper

Abstract: In this paper, we present a study of a Federated Learning (FL) system, based on the use of decentralized architectures to ensure trust and increase reliability. The system is based on the idea that the FL collaborators upload the (ciphered) model parameters on the Inter-Planetary File System (IPFS) and interact with a dedicated smart contract to track their behavior. Thank to this smart contract, the phases of parameter updates are managed efficiently, thereby strengthening data security. We have carried out an experimental study that exploits two different methods of weight aggregation, i.e., a classic averaging scheme and a federated proximal aggregation. The results confirm the feasibility of the proposal.

Source: IEEE International Conference on Blockchain (Blockchain)

DOI: 10.1109/blockchain62396.2024.00097

Authors: Lorenzo Cassano, Jacopo D'Abramo, Siraj Munir, Stefano Ferretti

Institutions: University of Bologna, University of Urbino

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

Awards: G2727155236, G972786147