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Federated learning (FL) enables multiple parties to collaboratively train machine learning models while preserving data privacy. However, securing communication within FL frameworks remains a significant challenge due to potential vulnerabilities to data breaches and integrity attacks. This paper proposes a novel approach using Dilithium, a robust digital signature framework, to enhance data security in FL. By integrating Dilithium into FL protocols, this study demonstrates robust communication security, preventing data tampering and unauthorized access, thereby promoting safer and more efficient collaborative model training across distributed networks. Furthermore, our approach incorporates an optimized client selection algorithm and a parallelized GPU-based training process that reduces latency and ensures seamless synchronization among participants. Experimental results demonstrate that our system achieves a total processing time of 6.891 seconds, significantly outperforming the 10.24 seconds of normal FL and 12.32 seconds of FL-Dilithium systems on the same computing platforms. Additionally, the proposed model achieves an accuracy of 94%, surpassing the 93% of the normal FL. 

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked convolutional neural networks (CNNs). Our innovative technique leverages the distributed nature of federated learning to collaboratively train a high-performance model while preserving data privacy on local devices. We propose a stacked CNN architecture tailored for ECG data, effectively extracting discriminative features across different temporal scales. The evaluation confirms the strength of our approach, culminating in a final model accuracy of 98.6% after 100 communication rounds, significantly exceeding baseline performance. This promising result paves the way for accurate and privacy-preserving ECG classification in diverse healthcare settings, potentially leading to improved diagnosis and patient monitoring. 

The development of fifth-generation (5G) technology marks a significant milestone for digital communication systems, providing substantial improvements in data transmission speeds and enabling enhanced connectivity across a wider range of devices. However, this rapid increase in data volume also introduces new challenges related to transmission latency, reliability, and security. This paper introduces KyMLP-LDPC, a novel approach that integrates a multi-layer parallel LDPC (MLP-LDPC) algorithm with Kyber, a post-quantum cryptography scheme, to accelerate and enable reliable and secure transmission. MLP-LDPC partitions the LDPC parity check matrix into processing groups to streamline parallel decoding and minimize message collisions during transmission, thereby accelerating error correction operations. Kyber encrypts data preemptively to safeguard against potential attacks. The effectiveness of our proposed method is evaluated using both image data and signals transmitted through an additive white Gaussian noise communication channel. Evaluation results demonstrate that the proposed method achieves superior performance in terms of error correction capabilities and data security compared to existing approaches. 

This paper addresses the challenges of data privacy and computational efficiency in artificial intelligence (AI) models by proposing a novel hybrid model that combines homomorphic encryption (HE) with AI to enhance security while maintaining learning accuracy. The novelty of our model lies in the introduction of a new matrix transformation technique that ensures compatibility with both HE algorithms and AI model weight matrices, significantly improving computational efficiency. Furthermore, we present a first-of-its-kind mathematical proof of convergence for integrating HE into AI models using the adaptive moment estimation optimization algorithm. The effectiveness and practicality of our approach for training on encrypted data are showcased through comprehensive evaluations of well-known datasets for air pollution forecasting and forest fire detection. These successful results demonstrate high model performance, with nearly 1 R-squared for air pollution forecasting and 99% accuracy for forest fire detection. Additionally, our approach achieves a reduction of up to 90% in data storage and a tenfold increase in speed compared to models that do not use the matrix transformation method. Our primary contribution lies in enhancing the security, efficiency, and dependability of AI models, particularly when dealing with sensitive data. 

Homomorphic encryption (HE) algorithms, particularly the Cheon-Kim-Kim-Song (CKKS) scheme, offer significant potential for secure computation on encrypted data, making them valuable for privacy-preserving machine learning. However, high latency in large integer operations in the CKKS algorithm hinders the processing of large datasets and complex computations. This paper proposes a novel strategy that combines lossless data compression techniques with the parallel processing power of graphics processing units to address these challenges. Our approach demonstrably reduces data size by 90% and achieves significant speedups of up to 100 times compared to conventional approaches. This method ensures data confidentiality while mitigating performance bottlenecks in CKKS-based computations, paving the way for more efficient and scalable HE applications.