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Explore advanced cryptographic techniques for secure computation and collaborative machine learning in this conference session from Crypto 2025. Delve into the intersection of outsourced computation and federated learning, examining how cryptographic protocols enable secure data processing across distributed systems while preserving privacy. Learn about cutting-edge research in secure multi-party computation, homomorphic encryption applications, and privacy-preserving machine learning frameworks that allow multiple parties to collaboratively train models without revealing their private data. Discover the latest developments in cryptographic protocols that support scalable and efficient federated learning systems, including techniques for handling malicious adversaries and ensuring data integrity in distributed environments. Gain insights into practical implementations and theoretical foundations of secure outsourced computation, with particular focus on how these methods apply to real-world federated learning scenarios where data privacy and computational efficiency are paramount.
