EVENTO
Private and Secure Federated Learning Protocol Based on Asymmetric DC-Nets
Tipo de evento: Defesa de Tese de Doutorado
Federated Learning (FL) enables collaborative machine learning across multiple clients without centralizing raw data, offering a promising approach for privacy-sensitive domains. However, standard FL protocols are vulnerable to inference attacks. While existing Secure Aggregation (SA) methods address this, they typically suffer from quadratic O(N²) communication overhead or the prohibitive computational costs of Homomorphic Encryption (HE). To overcome these fundamental bottlenecks, this thesis proposes and evaluates ADC-Fed, a highly scalable SA protocol that synergizes Asymmetric DC-Nets (ADC-Nets) with Multi-Secret Sharing (MSS) and Vandermonde interpolation. By completely abandoning heavy homomorphic operations, which were empirically demonstrated to impose a strict computational wall in an evaluated HE-based baseline (HE-ADC), ADC-Fed achieves information-theoretic privacy with strictly linear O(N) cryptographic communication complexity. Within the reported simulation environments and benchmark datasets, the evaluation demonstrates that ADC-Fed provides exact reconstruction of the intended aggregate, preserving the exact predictive utility of the non-private baseline without requiring interactive peer-to-peer dropout recovery. These results hold under the stated honest-but-curious server threat model, subject to the Minimum Honest Client Bound (MHC) where privacy is maintained provided at least two clients remain honest. Ultimately, this research validates ADC-Fed as a robust, high-performance architecture for privacy-preserving Federated Learning in resource-constrained and volatile network environments. Evento HíbridoLocal: Auditório LNCCLink de transmissão: meet.google.com/vch-gdpk-qkb
Data Início: 08/07/2026 Hora: 14:00 Data Fim: 08/07/2026 Hora: 17:00
Local: LNCC - Laboratório Nacional de Computação Ciêntifica - Auditorio B
Aluno: Paulo Ricardo Borré Reis - -
Orientador: Fábio Borges de Oliveira - Laboratório Nacional de Computação Científica - LNCC
Participante Banca Examinadora: Allan Jonathan da Silva - Laboratório Nacional de Computação Científica - LNCC Erick Giovani Sperandio Nascimento - University of Surrey - Fábio Borges de Oliveira - Laboratório Nacional de Computação Científica - LNCC Raphael Carlos Santos Machado - Universidade Federal Fluminense - UFF
Suplente Banca Examinadora: Lisandro Zambenedetti Granville - Universidade Federal do Rio Grande do Sul - UFRGS Renato Portugal - Laboratório Nacional de Computação Científica - LNCC
