| Title | An Approach for Peer-to-Peer Federated Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Wink, Tobias, Nochta, Zoltan |
| Conference Name | 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W) |
| Keywords | Collaboration, data protection, Deep Learning, federated learning, human factors, machine learning, Metrics, Mission critical systems, Neural networks, peer to peer security, peer-to-peer security, privacy, pubcrawl, resilience, Resiliency, Scalability, security, Stochastic processes, Training |
| Abstract | We present a novel approach for the collaborative training of neural network models in decentralized federated environments. In the iterative process a group of autonomous peers run multiple training rounds to train a common model. Thereby, participants perform all model training steps locally, such as stochastic gradient descent optimization, using their private, e.g. mission-critical, training datasets. Based on locally updated models, participants can jointly determine a common model by averaging all associated model weights without sharing the actual weight values. For this purpose we introduce a simple n-out-of-n secret sharing schema and an algorithm to calculate average values in a peer-to-peer manner. Our experimental results with deep neural networks on well-known sample datasets prove the generic applicability of the approach, with regard to model quality parameters. Since there is no need to involve a central service provider in model training, the approach can help establish trustworthy collaboration platforms for businesses with high security and data protection requirements. |
| DOI | 10.1109/DSN-W52860.2021.00034 |
| Citation Key | wink_approach_2021 |
An Approach for Peer-to-Peer Federated Learning