[ad_1] Introduction Blockchain technology has gained significant attention in recent years due to its potential to revolutionize various industries by providing decentralized, secure, and transparent systems. One area that stands to benefit greatly from the…
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[ad_1] Introduction In recent times, with the rapid advancements in technology and the widespread use of cloud computing, the need for secure and confidential data processing has become more important than ever. Encrypted computation is…
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[ad_1] Introduction Secure aggregation for distributed learning is a critical component in the field of machine learning and data privacy. With the increasing amount of data being collected and processed in various applications, the need…
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[ad_1] Introduction Federated analytics is a novel approach to decentralized learning that involves training machine learning models across multiple edge devices or servers while keeping the data on these devices rather than centralizing it. This…
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[ad_1] Introduction Homomorphic encryption is a groundbreaking technology that allows for computations to be performed on encrypted data without the need to decrypt it. This offers a powerful tool for privacy-preserving computation, ensuring that sensitive…
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[ad_1] Introduction: Distributed optimization for decentralized learning has gained increasing attention in recent years due to the proliferation of IoT devices and edge computing technologies. These technologies enable data to be processed closer to where…
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[ad_1] Introduction: Stochastic optimization is a powerful tool used in various fields such as machine learning, operations research, and engineering to find optimal solutions in the presence of uncertainty. In many real-world scenarios, the objectives…
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[ad_1] Introduction Non-convex optimization is a challenging field in mathematics and computer science that deals with finding the optimal solutions for problems that do not have convex objective functions. Local minima are a common issue…
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[ad_1] Introduction: In recent years, there has been a growing interest in submodular optimization for diversity and coverage in various fields such as machine learning, data mining, and artificial intelligence. Submodular functions have the property…
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[ad_1] Introduction: Matrix completion is a powerful tool used in the field of data analysis to estimate missing values within a given matrix. This technique has gained popularity in a variety of applications, such as…
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