Secure Federated Computation Models for Data Privacy in Distributed AI Systems
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I3P101Keywords:
Federated Learning, Secure Multi-Party Computation, Homomorphic Encryption, Data Privacy, Distributed Artificial Intelligence, Differential Privacy, Secure Aggregation, Adversarial Robustness, Trust-Weighted AggregationAbstract
Due to the growing adoption of distributed artificial intelligence (AI) systems, the shift towards decentralized learning systems has been facilitated by the paradigm of a centralized data repository being replaced by provisions of decentralized learning systems like Federated Learning (FL). Nonetheless, even though FL has promises of privacy, the fulfilment of privacy is threatened by many vulnerabilities such as gradient leakage, model inversion and communication attacks, which represent significant threats to data confidentiality and integrity. The study will introduce a flexible framework of models of Secure Federated Computation (SFC) taking into account the conscious integration of Homomorphic Encryption (HE), Secure Multi-party Computation (SMC), and Differential Privacy (DP) methods that will contribute to data protection in distributed AI frameworks. The model proposed tries to reduce the adversarial inference attacks and guarantee safe model aggregation without the need to reduce the computational efficiency. In this research, an adaptive federated computation protocol based on hybrid encryption mechanism is proposed, which can dynamically trade off the privacy guarantees and system throughput. A new function of trust-weighted aggregation is also implemented in the protocol to handle bad client behaviours and data poisoning attacks. The empirical analysis of the benchmark datasets such as CIFAR-10 and MNIST indicates that the proposed SFC model delivers a privacy leakage risk reduction of 42 based models at an equivalent model performance to regular FL systems. Scalability Analytical modelling and simulations verify that the architecture can be scaled to support large-scale, real-world applications in healthcare, finance, and IoT-driven settings. The researchers conclude that cryptographic computation can introduce a viable roadmap to secure and privacy-sensitive distributed AI ecosystems that meet the strict regulatory privacy requirements of initiatives like GDPR and HIPAA
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