Supervised, Unsupervised, Reinforcement, Deep learning, and federated learning (FL) are classical and contemporary techniques of machine learning. Each technique has its own advantages and disadvantages. FL does not exchange the local data samples across the edge devices, instead, it shares the model with other participating devices. Moreover, the Security and privacy of the network are also key factors in a wireless network. Therefore, Choosing an ML technique that also provides security and privacy is the need of the hour. My question is related to the security provided by federated learning. Is Federated learning secure as compared to other Machine learning techniques?
Dear Anil Kamboj
Compared to traditional centralized machine learning techniques that require data sets to reside on a single server, federated learning reduces data security and privacy concerns by maintaining stores of local data.
Kind Regards
Qamar Ul Islam
While developing (training) the model ( in distributed environment), the intruders may train the model with thier data also. While sharing the model, I hope there must be a mechanism to protect the model too.
i think so, because there are some studies is about security of network mangement through FL and blockchain.
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