Secure and Privacy-Preserving Decentralized Federated Learning for Personalized Recommendations in Consumer Electronics Using Blockchain and Homomorphic Encryption

Over the past few years, personalized recommendations have emerged as a fundamental component of the consumer electronics sector. The rise of decentralized federated learning has expanded the horizons of personalized recommendations, offering significant potential. Nonetheless, the utilization of confidential data from diverse clients raises legitimate concerns regarding privacy and security. In response to these challenges, we present an innovative framework for secure and privacy-preserving decentralized federated learning, tailored to personalized recommendations within the consumer electronics sector. Our approach strives to facilitate the collective contribution of data from multiple clients to the learning process while safeguarding their privacy. To accomplish this, we harness the power of homomorphic encryption, ensuring that clients' data remains encrypted and impervious to prying eyes. Additionally, we leverage blockchain technology to establish a secure, decentralized foundation for data exchange and management. Through the utilization of blockchain, we empower clients to validate the integrity of the learning process, guarantee system transparency, and thwart any malicious attempts at result manipulation. Our framework is rigorously assessed using real-world consumer electronics data, highlighting its capacity to provide a secure, decentralized, and privacy-centric solution for personalized recommendations. This approach not only enriches the user experience but also offers robust safeguards for sensitive data.