Secure peer-to-peer learning using feature embeddings

With more personal devices being connected to the internet, individuals are becoming increasingly concerned about privacy. Therefore, it is important to develop machine learning algorithms that can use customer data to create personalized models while still adhering to strict privacy laws. In this paper, we propose a robust solution to this problem in a distributed, asynchronous environment with a verifiable convergence rate. Our proposed framework trains a Convolutional Neural Network on each client and sends the feature embeddings to other clients for data aggregation. This allows each client to train a deep-learning model on feature embeddings gathered from various clients in a single communication cycle. We provide a detailed description of the architecture and execution of our suggested approach. Our technique's effectiveness is evaluated by comparing it to the top central training and federated learning (FL) algorithms, and our tests on diverse datasets demonstrate that our method outperforms FL in terms of accuracy and is comparable to central training algorithms. Our findings also show that our proposed method reduces data transfer by over 75% compared to FL, resulting in significant bandwidth savings. As a result, model training can assist companies with high security and data protection concerns in setting up reliable collaboration platforms without requiring a central service provider.