Data Dissemination in Wireless Sensor Networks with Network Coding

In wireless sensor networks (WSNs), it is often necessary to update the software running on sensors, which requires reliable dissemination of large data objects to each sensor with energy efficiency. During data dissemination, due to sleep scheduling designed for energy efficiency, some sensors may not receive some packets at some time slots. In the meantime, due to the unreliability of wireless communication, a sensor may not successfully receive a packet even when it is in the active mode. Thus, retransmission of such packets to those sensors is necessary, which consumes more energy and increases the delay of data dissemination cycle. In this paper, we propose a network coding-based approach in data dissemination such that data dissemination can be accomplished at the earliest time. Thus, less energy is consumed and the delay can be decreased. The impact of packet loss probability and the sleep probability of sensors on the network coding gain is analyzed. A threshold is also given to decide whether the current sleep scheduling is effective on energy saving in data dissemination process or not. Simulation results demonstrate the effectiveness and scalability of the proposed work

Advances and open problems in federated learning

Abstract Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this monograph discusses recent advances and presents an extensive collection of open problems and challenges