Federated Learning (FL) is a machine learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Despite its promises, FL is prone to critical security risks. First, because FL depends on a central server to aggregate local training models, this is a single point of failure. The server might function maliciously. Second, due to its distributed nature, FL might encounter backdoor attacks by participating clients. They can poison the local model before submitting to the server. Either type of attack, on the server or the client side, would severely degrade learning accuracy. We propose FedBlock, a novel blockchain-based FL framework that addresses both of these security risks. FedBlock is uniquely desirable in that it involves only smart contract programming, thus deployable atop any blockchain network. Our framework is substantiated with a comprehensive evaluation study using real-world datasets. Its robustness against backdoor attacks is competitive with the literature of FL backdoor defense. The latter, however, does not address the server risk as we do.

We present a design for a system that provides video-on-demand (VOD) services to mobile ad hoc clients. Such a system allows the clients to access video information anytime anywhere. MobiVoD, the proposed solution, overcomes many difficulties currently challenging video streaming in a mobile ad hoc network. The new environment includes a three-tier architecture, in which the mobile VOD system employs a periodic broadcast protocol to achieve maximum scalability; and the clients leverage an ad hoc network caching technique to minimize the service delay. This system can sustain client failure and mobility, and provide true VOD services to most clients.