Md. Shahriar Sajid

Md. Tanzib Hosain, Asif Zaman, Md. Shahriar Sajid et al.

The widespread adoption of Artificial Intelligence (AI) has been driven by significant advances in intelligent system research. However, this progress has raised concerns about data privacy, leading to a growing awareness of the need for privacy-preserving AI. In response, there has been a seismic shift in interest towards the leading paradigm for training Machine Learning (ML) models on decentralized data silos while maintaining data privacy, Federated Learning (FL). This research paper presents a comprehensive performance analysis of a cutting-edge approach to personalize ML model while preserving privacy achieved through Privacy Preserving Machine Learning with the innovative framework of Federated Personalized Learning (PPMLFPL). Regarding the increasing concerns about data privacy, this study evaluates the effectiveness of PPMLFPL addressing the critical balance between personalized model refinement and maintaining the confidentiality of individual user data. According to our analysis, Adaptive Personalized Cross-Silo Federated Learning with Differential Privacy (APPLE+DP) offering efficient execution whereas overall, the use of the Adaptive Personalized Cross-Silo Federated Learning with Homomorphic Encryption (APPLE+HE) algorithm for privacy-preserving machine learning tasks in federated personalized learning settings is strongly suggested. The results offer valuable insights creating it a promising scope for future advancements in the field of privacy-conscious data-driven technologies.

Md Shahriar Sajid, Abhijit Kumar Ghosh, Fariha Nusrat

Electroencephalography (EEG) signals contain rich temporal-spectral structure but are difficult to model due to noise, subject variability, and multi-scale dynamics. Lightweight deep learning models have shown promise, yet many either rely solely on local convolutions or require heavy recurrent modules. This paper presents PaperNet, a compact hybrid architecture that combines temporal convolutions, a channel-wise residual attention module, and a lightweight bidirectional recurrent block which is used for short-window classification. Using the publicly available BEED: Bangalore EEG Epilepsy Dataset, we evaluate PaperNet under a clearly defined subject-independent training protocol and compare it against established and widely used lightweight baselines. The model achieves a macro-F1 of 0.96 on the held-out test set with approximately 0.6M parameters, while maintaining balanced performance across all four classes. An ablation study demonstrates the contribution of temporal convolutions, residual attention, and recurrent aggregation. Channel-wise attention weights further offer insights into electrode relevance. Computational profiling shows that PaperNet remains efficient enough for practical deployment on resource-constrained systems through out the whole process. These results indicate that carefully combining temporal filtering, channel reweighting, and recurrent context modeling can yield strong EEG classification performance without excessive computational cost.