Scope

The journal Artificial Intelligence and Autonomous Systems (AIAS) is an online multidisciplinary open access journal aiming to provide a peer-reviewed forum for rigorous and fast publications of the latest research findings and industrial applications in the contemporary fields of AI and autonomous systems. AIAS welcomes research articles on the theoretical, computational, cognitive, and empirical aspects of AI, autonomous systems, and their implementations.

The scope and topics of AIAS include but are not limited to:

Theoretical foundations of AI
Theoretical foundations of AS
Autonomous AI
Brain-inspired systems
Autonomous medical devices and systems
Autonomous vehicles
Autonomous human-machine systems
Autonomous function and behavior generation
Interactive intelligent systems
Autonomous decision making
Autonomous machine learning theory
Computer vision
Autonomous robotics and control
Language and semantic processing
Data science
AI control theory and optimization
Networked and distributed systems
AI-based computer security
High-performance computing driven by AI

Special Issue on Federated Learning for Secure and Privacy-Preserving Intelligent Systems
Submission Date: 2026-08-31

The rapid proliferation of intelligent systems across healthcare, finance, transportation, and industrial automation has led to unprecedented volumes of sensitive data. While these datasets fuel the development of advanced machine learning models, traditional centralized learning paradigms raise significant concerns regarding privacy, data security, and regulatory compliance. Federated Learning (FL) has emerged as a transformative paradigm that enables collaborative model training across distributed devices or institutions without the need to share raw data. By keeping data localized while sharing model updates, FL offers a promising solution for privacy-preserving intelligence, secure decision-making, and decentralized AI.

Recent advancements in FL have extended its potential beyond basic collaborative learning. Novel algorithms now address issues such as communication efficiency, heterogeneity of data distributions, robustness against adversarial attacks, and formal privacy guarantees through differential privacy and secure multi-party computation. These developments are reshaping the landscape of intelligent systems, enabling scalable deployment of AI in sensitive domains while maintaining regulatory compliance and user trust.This special issue aims to consolidate cutting-edge research, innovative methodologies, and real-world applications of federated learning in the context of secure and privacy-aware intelligent systems. We invite contributions that not only advance theoretical understanding but also demonstrate practical impact in deploying FL in real-world scenarios.

Topics of interest include, but are not limited to:

Federated learning algorithms for heterogeneous and non-i.i.d. data
Privacy-preserving techniques in FL, including differential privacy and homomorphic encryption
Secure aggregation, blockchain-enabled FL, and adversarially robust FL
Communication-efficient and scalable FL for edge and IoT devices
Federated learning in healthcare, finance, smart cities, and industrial systems
Model personalization and transfer learning in federated settings
Benchmarking, evaluation metrics, and empirical studies of FL under security and privacy constraints
Interdisciplinary approaches combining FL with reinforcement learning, computer vision, NLP, or multimodal AI

We particularly encourage submissions that bridge theoretical innovation and practical deployment, demonstrating how federated learning can enable secure, trustworthy, and privacy-respecting intelligent systems across diverse application domains.

The journal is preparing for inclusion in major indexing services (e.g., Scopus, SCI). Early publications will be automatically included once indexing is granted. Authors benefit from waived article processing charges, and early participation as a founding contributor helps establish the journal’s impact from its inception.