What question did this study set out to answer?

The survey aims to explore the integration of explainable AI (XAI) in distributed and federated Internet of Things (IoT) architectures and identify evaluation gaps.

February 6, 2026Open Access

Next-Gen Explainable AI (XAI) for Federated and Distributed Internet of Things Systems: A State-of-the-Art Survey

Key Points

The survey aims to explore the integration of explainable AI (XAI) in distributed and federated Internet of Things (IoT) architectures and identify evaluation gaps.
Conducted a structured search across major academic databases like IEEE Xplore and ScienceDirect.
Searched for publications related to XAI, IoT, edge computing, and federated learning.
Synthesized relevant studies based on deployment tier, explanation scope, and validation methodology.
Identified a resource–interpretability gap, with complex explainers misapplied in edge and federated environments.
Few studies measure privacy–utility effects, limiting explanation reliability in critical IoT applications.
Introduced a new evaluation framework with metrics like Computational Complexity Score and Memory Footprint Ratio.

Abstract

Background: Explainable Artificial Intelligence (XAI) is deployed in Internet of Things (IoT) ecosystems for smart cities and precision agriculture, where opaque models can compromise trust, accountability, and regulatory compliance. Objective: This survey investigates how XAI is currently integrated into distributed and federated IoT architectures and identifies systematic gaps in evaluation under real-world resource constraints. Methods: A structured search across IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Google Scholar targeted publications related to XAI, IoT, edge/fog computing, smart cities, smart agriculture, and federated learning. Relevant peer-reviewed works were synthesized along three dimensions: deployment tier (device, edge/fog, cloud), explanation scope (local vs. global), and validation methodology. Results: The analysis reveals a persistent resource–interpretability gap: computationally intensive explainers are frequently applied on constrained edge and federated platforms without explicitly accounting for latency, memory footprint, or energy consumption. Only a minority of studies quantify privacy–utility effects or address causal attribution in sensor-rich environments, limiting the reliability of explanations in safety- and mission-critical IoT applications. Contribution: To address these shortcomings, the survey introduces a hardware-centric evaluation framework with the Computational Complexity Score (CCS), Memory Footprint Ratio (MFR), and Privacy–Utility Trade-off (PUT) metrics and proposes a hierarchical IoT–XAI reference architecture, together with the conceptual Internet of Things Interpretability Evaluation Standard (IOTIES) for cross-domain assessment. Conclusions: The findings indicate that IoT–XAI research must shift from accuracy-only reporting to lightweight, model-agnostic, and privacy-aware explanation pipelines that are explicitly budgeted for edge resources and aligned with the needs of heterogeneous stakeholders in smart city and agricultural deployments.

Next-Gen Explainable AI (XAI) for Federated and Distributed Internet of Things Systems: A State-of-the-Art Survey

Key Points

Abstract

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