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Artificial Intelligence and Autonomous Systems

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출판사:
ELSP
ISSN:
2959-0744
조회:
13711
팔로우:
1

논문 모집

Artificial Intelligence and Autonomous Systems is an academic journal published by ELSP. (ISSN 2959-0744).

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
최종 수정: Dou Sun ()

Special Issues

Special Issue on Federated Learning for Secure and Privacy-Preserving Intelligent Systems 투고 마감일: 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.
최종 수정: Dou Sun ()

이 항목을 본 사람들이 함께 본 항목

CCF정식 명칭영향력 지수출판사ISSN
IEEE Security & Privacy3.0IEEE1540-7993
CNeural Computing and ApplicationsSpringer0941-0643

관련 저널

CCF정식 명칭영향력 지수출판사ISSN
AI+ELSP3007-7443
BlockchainELSP2959-1260
Advanced Information and CommunicationELSP3106-1443
Mechatronics TechnologyELSP2959-376X
Robotics and Autonomous Systems5.2Elsevier0921-8890
Advanced ManufacturingELSP2959-3263
AI & MaterialsELSP3006-7588
Electronics and Signal ProcessingELSP2959-913X
Biomedical InformaticsELSP3005-3862
Robot LearningELSP2960-1436

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