ManitobaSat-1: A novel approach for technology advancement

July 1, 2020 by Amin Yahyaabadi, Matthew Driedger, Nathalie Turenne, Stephanie Connell, Varsha Parthasarathy, Aimee Carvey, Valorie Platero, Jaime Campos, Michael Rososhansky, and Philip Ferguson

ManitobaSat-1 is a 3U CubeSat program that is determined to advance the edges of space technology by performing things differently from traditional design strategies (see Fig.1). This will enhance access to space through novel technology development and verification and enable smaller companies to benefit from satellite services. ManitobaSat-1 is being developed at the University of Manitoba by the Space Technologies and Advanced Research Laboratory (STARLab) in collaboration with the Center for Terrestrial and Planetary Exploration at the University of Winnipeg, York University, the Interlake School Division, and Magellan Aerospace, our industrial partner.

For more about this article see link below.

https://ieeexplore.ieee.org/document/9132729

Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.

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