Federated Learning Architectures for Privacy-Preserving Collaborative Intelligence in Distributed Networks
DOI:
https://doi.org/10.64137/XXXXXXXX/IJCSEI-V1I1P104Keywords:
Federated learning, Privacy-preserving, Machine learning, Differential privacy, Secure multi-party computation, Homomorphic encryption, Decentralized architecturesAbstract
Federated Learning offers a new solution that helps different networks to cooperate while maintaining user privacy in key areas such as healthcare, finance, and IoT. In FL, individuals or organizations receive training locally and only share encrypted changes to the model, which helps them avoid centralized data dangers and supports working together with others. The approach relies on several frameworks, like federated learning over time, federated learning across clients, federated transfer learning, for data that matches various transaction types. To protect against data leakage and hackers, FL systems use differential privacy (adding noise to gradients), secure multi-party computation (combining data while it remains encrypted), and homomorphic encryption (allowing calculations directly on encrypted data). Even though these improvements have been made, difficulties still arise in working with non-IID data, dealing with diverse systems, and increasing communication time. It has been found in experiments that FL can achieve similar accuracy (for example, 97.3% on important datasets) as centralized methods, yet it offers important increases in privacy. Federated Learning is useful in medical navigation, stopping fraud, and making infrastructure smart, as it permits different models to be trained together without revealing confidential details. Future research will focus on using better ways to communicate, advancing privacy-utility balances with personalized new FL methods, and including quantum computing as much as possible in distributed networks
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