Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3770 2019 2019 Research Scholar, Department of CSE, Karpagam Academy of Higher Education Coimbatore, India D. D. Associate professor Department of CSE Karpagam Academy of Higher Education Coimbatore, India M. Vigenesh In recent years, federated learning (FL) has emerged as a decentralized approach to model training, enhancing data privacy by retaining data on local edge devices. While existing privacy-preserving FL frameworks, like Secure Aggregation and Homomorphic Encryption, protect data through encrypted aggregation, they often face challenges with high communication overhead, significant computational demands, and increased energy consumption. Differential privacy approaches, though customizable via privacy budgets, may also degrade model accuracy due to added noise. Addressing these limitations, we propose PrivaNet-FL (Privacy-Optimized Network for Federated Learning), an advanced FL model that optimizes privacy techniques with minimal energy costs in edge environments. PrivaNet-FL incorporates adaptive privacy and efficiency management across edge devices, such as IoT sensors and smartphones, where data processing and real-time privacy adjustments conserve energy while maintaining data security. The framework consists of three main workflows: (1) Adaptive Privacy-Scaling-modulating privacy based on device constraints, ensuring optimal energy usage through dynamic adjustments of noise in differential privacy or encryption complexity; (2) Lightweight Encryption and Secure Aggregation-employing low-complexity encryption and secure aggregation techniques, such as random masking and distributed averaging, to minimize energy without compromising data privacy; and (3) Energy-Aware Communication-Efficient FL-leveraging model compression, energy-aware scheduling, and differential privacy with controlled noise to reduce communication and energy overhead. Results demonstrate that PrivaNet-FL achieves superior model accuracy with reduced energy and communication costs compared to traditional FL methods, making it ideal for privacy-sensitive and resource-limited edge applications. 2025 2025 28 46 10.54216/JCIM.160203 https://www.americaspg.com/articleinfo/2/show/3770