Poster: A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain

Federated learning (FL) is promising in supporting collaborative learning applications that involve large datasets, massively distributed data owners and unreliable network connectivity. To protect data privacy, existing FL approaches adopt (k, n)-threshold secret sharing schemes, based on the semi-honest assumption for clients, to enable secure multiparty computation in local model update exchange which deals with random client dropouts at the cost of increasing data size. These approaches adopt the semi-honest assumption for clients, therefore they are vulnerable to malicious clients. In this work, we propose a blockchain-based privacy-preserving federated learning (BC-based PPFL) framework, which leverages the immutability and decentralized trust properties of blockchain to provide provenance of model updates. Our proof-of-concept implementation of BC-based PPFL demonstrates it is practical for secure aggregation of local model updates in the federated setting.