New computer code for mechanics of tissues and cells in three dimensions phys.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from phys.org Daily Mail and Mail on Sunday newspapers.
Mobile crowdsensing (MCS) combined with federated learning, as an emerging data collection and intelligent process paradigm, has received lots of attention in social networks and mobile Internet-of-Things, etc. However, as the openness and transparent of mobile crowdsensing tasks, federated learning model and training samples for crowdsensing data still face enormous privacy revealing risks, and it will reduce the willingness of people or nodes to actively participate and provide data in MCS. In this paper, we present a Privacy-Enhanced Aggregation for Federated Learning in MCS, namely PrivacyEAFL, to implement the training of federated learning under mobile crowdsensing system in terms of privacy protection of all participants. Firstly, considering that the crowdsensing server might share information with some participants to obtain and leak some local models, we design a collusion-resistant data aggregation approach by combining homomorphic cryptosystem and hashed Diffie-Hellman key
The rapid development of both hardware and software has promoted the popularization of various real-time applications like health monitoring and intrusion detection that are widely deployed in outsourcing scenarios, e.g., mobile edge computing and cloud computing. In these applications, end devices continuously generate unbounded sequences of data items at a fast rate, i.e., the so-called streaming data. Nevertheless, storing and processing massive amounts of streaming data poses a challenge for resources-restricted end devices. Although outsourcing data items to edge servers or cloud servers is an attractive solution to the above problem, it also brings a new challenge, i.e., how to guarantee the integrity of outsourced data, since streaming data applications are usually sensitive of both location and the corresponding context, and servers are not completely trusted. To this end, the primitive of verifiable data streaming (VDS) protocol was introduced to maintain outsourced streaming
Professor Daniel Mugendi Njiru, Rector of the University of Embu, congratulates Professor Anna Matuszyńska on her successful workshop at the 2023 Hackathon on Computational ModelingHow can computer-aided modeling techniques be integrated into researc
Special Section on Computational Modeling for Microwave Processing and Characterization of Materials mtt.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from mtt.org Daily Mail and Mail on Sunday newspapers.