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Dettaglio pubblicazione

2023, IFAC-PapersOnLine, Pages -868 (volume: 56)

Hierarchical Federated Learning for Edge Intelligence through Average Consensus (04b Atto di convegno in volume)

Menegatti Danilo, Manfredi Sabato, Pietrabissa Antonio, Poli Cecilia, Giuseppi Alessandro

Consensus of multi-agent systems has recently been studied in the context of Federated Learning (FL), an emerging branch of distributed machine learning. The present paper proposes a two-level hierarchical algorithm for FL in the context of edge computing, developing a fully decentralized solution that relies on results obtained for discrete-time consensus of dynamical systems. The proposed architecture and algorithm are validated on a test case and compared to current solutions, which require a centralized server.
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