MOSAIC-FL, a Micro-Service Based Privacy-Preserving Framework with Application to Genomics

BLOCKCHAIN-BASED PRIVACY-PRESERVING AND SECURE FEDERATED LEARNING FRAMEWORK

A block chain-based Privacy-preserving and Secure Federated Learning (BPS-FL) system that uses threshold homomorphic encryption to safeguard the local gradients of clients in order to successfully solve such privacy and security assault challenges is suggested.

PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning

This work proposes PRoVeFL-a novel, modular FL framework that is Privacy-preserving, Byzantine-Robust, and ensures Verifiable aggregation, and improves runtime over the prior works, Prio and ELSA, based on distributed trust with comparable security guarantees, up to 100x and 10x, respectively.

LLMs AND DATA PRIVACY

A Survey on Privacy-Preserving Techniques for Cloud Data Processing Using Homomorphic Encryption and Federated Learning

Cloud computing offers organizations scalable storage and computation, but outsourcing data processing to third-party infrastructure introduces serious privacy and confidentiality risks. Two complementary paradigms have emerged to address this challenge: homomorphic encryption (HE), which allows computation directly on encrypted data, and federated learning (FL), which enables collaborative model training without centralizing raw data. This paper presents a structured review of privacy-preserving data processing techniques for cloud environments built on HE and FL, individually and in hybrid combination. We propose a taxonomy of existing approaches, synthesize representative literature in a comparative table, illustrate a generic hybrid HE-FL architecture, and evaluate the two paradigms against criteria including data exposure, computational overhead, communication cost, resistance to inference attacks, and cloud deployment readiness. We further identify open challenges — including computational latency, key management, non-IID data distributions, and standardization gaps — and outline promising directions for future research, such as hardware-accelerated HE, adaptive encryption granularity, and standardized hybrid privacy frameworks for cloud-native machine learning.

FIDES: A Federated Intelligence and Detection with Quantum Security for Financial Institutions

With the increasing sophistication of financial frauds, there is now a need for more advanced, secure, and scalable detection mechanisms. A fraud detection framework has been proposed that uses Federated Deep Learning (FDL) and Quantum Key Distribution (QKD) for non-IID financial data while carrying out secure communication. Using FL algorithms-FedAvg, FedAdagrad, FedAMP, and FedDyn-on partitioned client data, we demonstrate that FedDyn outperforms the other algorithms with an accuracy of 97.74%. Furthermore, we use Continuous-Variable QKD to encrypt the model updates to secure client-server communication, achieving a secure key ratio of above 98% and key rates of more than 250,000 bits/sec. Lastly, we implemented an elaborate suite of evaluations consisting of client-wise metrics, ROC curves, and t-SNE plots to validate the efficacy of our model implementation in terms of both performance and privacy preservation. Through our results, we address the brought-up importance of distributed intelligence powered by quantum encryption against advanced financial frauds.

A Federated Deep Learning Paradigm for Privacy-Preserving Cloud Applications

Deep learning is becoming popular in cloud applications and serves to provide intelligent services; data aggregation in a central location makes sensitive information vulnerable to privacy breaches, regulatory infractions, and adversarial manipulation. All modern privacy mechanisms offer partial protection and frequently lack accuracy, scalability, or practicality in their operations. To overcome these limitations, a federated deep learning model is formulated so that secure joint learning can occur without transferring raw data across the domains of ownership. The framework incorporates training that is decentralized, training that uses differential privacy, training that uses secure aggregation, training that uses encrypted communication, and training that uses trust-based anomaly defense to defend against leakage, poisoning, and inference attacks. It also supports heterogeneous and highly non-IID datasets using adaptive coordination and stability-relevant participation regulation and meets emerging data protection requirements. The methods of resource-conscious orchestration and the optimization of communication eliminate overhead without obstructing the effectiveness of learning. The paradigm has therefore formed a privacy-by-design intelligent cloud ecosystem which ensures confidentiality, maintains performance, enhances robustness, and ensures responsible AI implementation in privacy-related sectors of healthcare, finance, governance, and smart infrastructure.