Computer Science · Literature

Research papers on Federated learning and privacy

Recent and highly-cited academic work on federated learning and privacy, gathered from Semantic Scholar, CrossRef and OpenAlex.

1. Federated Learning for Privacy-Preserving AI

   RN Shashi Vardhan · 2025 · International Journal of Research Publication and Reviews · 3 citations

   The rapid adoption of Artificial Intelligence (AI) across industries, particularly in healthcare, finance, and smart devices, has introduced significant concerns regarding data privacy, security, and compliance with regulations such as GDPR, HIPAA, and CCPA. Traditional centralized machine learning (ML) models require large-scale data aggregation, increasing risks of data breaches, misuse, and unauthorized access. Federated Learning (FL) has emerged as a transformative solution, allowing multiple edge devices or organizations to collaboratively train machine learning models without sharing raw data. This paper explores the principles, advantages, and challenges of FL and conducts an

   Save this paper

2. Federated Learning for Privacy-Preserving Employee Performance Analytics

   Jay Barach · 2025 · IEEE Access · 3 citations

   Save this paper

3. Federated Learning: Privacy-Preserving Machine Learning in Cloud Environments

   Bangar Raju Cherukuri · 2024 · International Journal of Science and Research (IJSR) · 1 citations

   Save this paper

4. Federated Learning Architectures for Privacy Preserving Financial Fraud Detection Systems

   Favour . C. Ezeugboaja · 2025 · Frontiers in Emerging Artificial Intelligence and Machine Learning · 1 citations

   The increasing complexity and intensity of cases of financial fraud, such as synthetic identity fraud and international money laundering, have become significant concerns for classic fraud detection solutions, especially under strict data privacy regulations such as GDPR or EU Artificial Intelligence Act guidelines. This study focuses on the very pressing need to pursue high fraud detection performance while simultaneously ensuring user data confidentiality for highly fragmented financial systems. The study uses federated learning (FL) designs to analyse interesting opportunities for possibly entirely decentralized machine learning functions among diverse financial agencies without any need

   Save this paper

5. Privacy Preserving Federated Learning Efficiency Optimization Algorithm based on Differential Privacy

   Rui Xie · 2025 · Computer Fraud and Security · 1 citations

   With the advancement of information technology, data security and user privacy protection have become paramount. To achieve efficient privacy protection in a federated learning environment, a differential privacy algorithm is designed using the eXtreme Gradient Boosting (XGBoost) algorithm. This algorithm optimizes the privacy protection process by applying differential privacy to the optimal segmentation point in a weak classifier. Additionally, to address the multi-party collaboration challenge in federated learning, a differential privacy construction scheme based on multi-party collaboration is proposed. The results indicate that the running times of differential privacy algorithms based

   Save this paper

6. Federated Learning for Privacy-Preserving Big Data Analytics in Distributed Systems

   Ahmed Gheni Dawood, Ekhlas Muthanna Turki · 2026 · JOINCS (Journal of Informatics, Network, and Computer Science)

   Federated Learning (FL) is an important concept in big data analytics because it has changed the way collaborative model training can be done on devices that are decentralized while ensuring user privacy, an essential requirement in an accurate evidence-based and regulated environment with even stricter requirements from regulations like GDPR, HIPAA, CCPA and future laws on data sovereignty. This paper analyzed FL in depth. It described foundational concepts, architectural approaches, algorithmic approaches, real-world and practical applications and challenges in distributed systems. Key issues such as communication overhead, data heterogeneity, security risks, fairness, scalability, energy

   Save this paper

7. Privacy-preserving federated learning with differential privacy for healthcare AI: a convergence and utility analysis

   Aruna Pavate · 2025 · Journal of Artificial Intelligence Machine Learning and Neural Network

   Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without sharing raw patient data, offering a paradigm shift for privacy-sensitive medical AI. However, FL remains vulnerable to gradient inversion attacks and model poisoning, necessitating formal privacy guarantees. This paper presents a comprehensive analysis of Differential Privacy (DP)-augmented Federated Learning for healthcare AI applications, specifically Electronic Health Record (EHR) classification. We evaluate three aggregation strategies FedAvg, FedProx, and the proposed FedNova-DP across simulated environments with 10, 25, and 50 heterogeneous clients under both IID and non-IID

   Save this paper

8. Fedssl: privacy-preserving federated self-supervised learning with differential privacy guarantees for heterogeneous edge environments

   Zayyanu Yunusa · 2025 · Journal of Artificial Intelligence Machine Learning and Neural Network

   In this study, we delve into the significant impact of AI, investigating its multifaceted consequences on society. In a rapidly evolving digital landscape, the integration of artificial intelligence has emerged as a transformative force in reshaping human progress and well-being. Drawing from historical perspectives, we trace the evolution of AI and its transformative journey. Our research aims to comprehensively analyze approaches for fostering responsible AI growth while mitigating potential hazards. By illuminating both the promise and perils of AI, this study contributes to informed decision-making in the unfolding AL era. This research explores the innovative utilization of AI technolog

   Save this paper