Toward Explainable and Trustworthy Federated Big Data Intelligence in Secure Edge-Cloud Ecosystems: A Conceptual Framework and Research Agenda

Abstract

Edge-cloud big data ecosystems are changing many areas, such as healthcare, smart cities, manufacturing IoT settings, cybersecurity, self-driving systems, mobile platforms, and financial analytics. Additionally, these groups produce a lot of data that is dispersed, unique, changes based on the situation, is sensitive to delays, and is usually private. Classical centralized cloud analytics are having more and more issues with bandwidth, latency, single points of failure, and keeping private data and power to make decisions in one place. Multiple edge clients can work on the same model at the same time with federated learning, and the raw data stays close by. Even so, federated learning doesn't guarantee accurate knowledge by itself. Although model changes can keep private data safe, hacked clients can ruin global learning, non-IID data can lead to performance gaps, and choices made by many people may still be hard to explain, audit, or control. This article suggests a trustworthy way to think about shared big data information that can be communicated in secure edge-cloud settings. Additionally, it includes studies on edge-cloud computing, shared learning, AI that can be explained, privacy-preserving analytics, security, and AI control. Additionally, the paper includes a study plan, an assessment tool, a trust-risk mapping, evaluation factors, and governance development standards. When making a case, the main point is that trustworthy federated intelligence is more than just distributed learning that protects privacy. It's a sociotechnical paradigm that includes privacy protection, security robustness, explanation quality, accountability, auditability, fairness, the ability to be deployed, and meaningful human oversight.