همايش ملی پيشرفت های معماری سازمانی

صفحه اصلی / ششمین همايش ملی پيشرفت های معماری سازمانی

Federated Learning: Solutions, Challenges, and Promises

نویسندگان :

Reza Fotohi¹ Fereidoon Shams Aliee² Bahar Farahani³

1- دانشگاه شهید بهشتی 2- دانشگاه شهید بهشتی 3- دانشگاه شهید بهشتی

کلمات کلیدی :

Privacy-Preserving،Machine Learning،Federated learning،Blockchain،Homomorphic Encryption

چکیده :

In federated learning (FL), a model is trained by monitoring and managing a server by a group of clients, so that the data trained in this way is stored in a decentralized manner. Therefore, each client uses its private data to train its local model. Then each client uploads their updated model to the server with the aim of aggregating and building the final global model. Finally, after reaching the desired threshold, the server updates the global model and then sends the final updated global model to the clients. This process is repeated until the desired threshold/accuracy is reached. In fact, the purpose of FL is to preserve the privacy of data generated by clients, which are stored and processed locally and only periodically provide updates of local models to the server. This paper provides a comprehensive overview of FL with an emphasis on combining FL with other technologies and techniques. Since FL is vulnerable to several attacks, researchers combined the FL concept with approaches such as homomorphic encryption and blockchain. Here, we divided the existing solutions into the categories of FL, the fusion of blockchain and FL, the fusion of FL and homomorphic encryption, and the fusion of FL, homomorphic encryption, and blockchain. In addition, for all the compared approaches, their advantages and disadvantages were explained, and based on these, open problems were highlighted.