Security and Privacy Preservation via Interference Tolerant Fast Convergence Zeroing Neural Network With Reptile Search Optimization Algorithm in Fog‐Cloud Computing DOI

P Kumar,

Neha Verma, Shivani Gupta

и другие.

Transactions on Emerging Telecommunications Technologies, Год журнала: 2025, Номер 36(4)

Опубликована: Апрель 1, 2025

ABSTRACT More vertical service areas than only data processing, storing, and communication are promised by fog‐cloud computing. Due to its great efficiency scalability, distributed deep learning (DDL) across computing environments is a widely used application among them. With training limited sharing parameters, DDL can offer more privacy protection centralized learning. Nevertheless, still faces two significant security obstacles when it comes How ensure that users' identities not stolen outside enemies, prevent from being disclosed other internal participants in the process of training. In this manuscript, Interference Tolerant Fast Convergence Zeroing Neural Network for Security Privacy Preservation with Reptile Search Optimization Algorithm Fog‐Cloud Computing environment (SPP‐ITFCZNN‐RSOA‐FCC) proposed. ITFCZNN proposed preservation, Then (RSOA) optimize ITFCZNN, Effective Lightweight Homomorphic Cryptographic (ELHCA) encrypt decrypt local gradients. The SPP‐ITFCZNN‐RSOA‐FCC system attains better balance, efficiency, functionality existing efforts. implemented using Python. performance metrics like accuracy, resource overhead, computation overhead considered. approach 29.16%, 20.14%, 18.93% high 11.03%, 26.04%, 23.51% lower Resource compared methods including FedSDM: Federated dependent smart decision making component ECG at internet things incorporated Edge‐Fog‐Cloud (SPP‐FSDM‐FCC), A collaborative offloading dew‐enabled vehicular fog compute‐intensive latency‐sensitive dependence‐aware tasks: Q‐learning method (SPP‐FDQL‐FCC), fog‐edge‐enabled intrusion identification scheme grids (SPP‐FSVM‐FCC) respectively.

Язык: Английский

Security and Privacy Preservation via Interference Tolerant Fast Convergence Zeroing Neural Network With Reptile Search Optimization Algorithm in Fog‐Cloud Computing DOI

P Kumar,

Neha Verma, Shivani Gupta

и другие.

Transactions on Emerging Telecommunications Technologies, Год журнала: 2025, Номер 36(4)

Опубликована: Апрель 1, 2025

ABSTRACT More vertical service areas than only data processing, storing, and communication are promised by fog‐cloud computing. Due to its great efficiency scalability, distributed deep learning (DDL) across computing environments is a widely used application among them. With training limited sharing parameters, DDL can offer more privacy protection centralized learning. Nevertheless, still faces two significant security obstacles when it comes How ensure that users' identities not stolen outside enemies, prevent from being disclosed other internal participants in the process of training. In this manuscript, Interference Tolerant Fast Convergence Zeroing Neural Network for Security Privacy Preservation with Reptile Search Optimization Algorithm Fog‐Cloud Computing environment (SPP‐ITFCZNN‐RSOA‐FCC) proposed. ITFCZNN proposed preservation, Then (RSOA) optimize ITFCZNN, Effective Lightweight Homomorphic Cryptographic (ELHCA) encrypt decrypt local gradients. The SPP‐ITFCZNN‐RSOA‐FCC system attains better balance, efficiency, functionality existing efforts. implemented using Python. performance metrics like accuracy, resource overhead, computation overhead considered. approach 29.16%, 20.14%, 18.93% high 11.03%, 26.04%, 23.51% lower Resource compared methods including FedSDM: Federated dependent smart decision making component ECG at internet things incorporated Edge‐Fog‐Cloud (SPP‐FSDM‐FCC), A collaborative offloading dew‐enabled vehicular fog compute‐intensive latency‐sensitive dependence‐aware tasks: Q‐learning method (SPP‐FDQL‐FCC), fog‐edge‐enabled intrusion identification scheme grids (SPP‐FSVM‐FCC) respectively.

Язык: Английский

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