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ICPADS 2026: International Conference on Parallel and Distributed Systems

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ICPADS
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Submission Date:
2026-06-30
Notification Date:
2026-09-01
Conference Date:
2026-11-22
Location:
Tokyo, Japan
Years:
32
CCF: C   ICORE: B   QUALIS: B1   Viewed: 286565   Tracked: 305   Attend: 92

Conference Partner Index (CP-I)

80.3 / 100
Ranked #191 of 5,645 conferences · Top 4%

#38 of 340 in Systems & Architecture

Academic recognition (35%)
78
Submission selectivity (20%)
79
Editions held (20%)
94
Community attention (10%)
87
Public record completeness (15%)
65

Inputs used: Listed as CCF C, ICORE B, QUALIS B1 · Acceptance rate: 29.7% (mean of 2 editions on file) · Editions on record: 32 · Researchers following it here: 305 · Researchers who opened this page in the past 24 months: 70

Missing from the public record: Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-01

Call For Papers

ICPADS 2026 (International Conference on Parallel and Distributed Systems) is a CCF C / ICORE B / QUALIS B1 conference held in Tokyo, Japan on 2026-11-22. The paper submission deadline is 2026-06-30. Acceptance notifications are sent on 2026-09-01.

TRACKS AI Infrastructure and Systems Scope The track of AI Infrastructure and Systems aims to bring together researchers and practitioners working on the foundations, architectures, and system-level innovations that power modern artificial intelligence. As AI models continue to grow in scale and complexity, there is an urgent need for robust, scalable, and efficient infrastructure to support training, deployment, and lifecycle management of AI workloads. This track focuses on the design, implementation, and optimization of AI-centric systems spanning cloud, edge, and hybrid environments. We welcome research that addresses the essential challenges of large-scale model training, distributed inference, heterogeneous hardware acceleration, resource orchestration, system reliability, and observability. We are particularly interested in research work that bridges AI algorithms and system design, enabling co-optimization across hardware, software, and networking layers. Our motivation is to foster cross-disciplinary dialogue between systems researchers, AI practitioners, hardware designers, and industry engineers to advance next-generation AI infrastructure that is scalable, reliable, trustworthy, and efficient. Topics of Interest Our track seeks original contributions in the following topical areas, plus others that are not explicitly listed but are closely related: Distributed and large-scale training systems for foundation models AI-native cloud and data center architectures Reliability, availability, and serviceability of AI systems Observability for AI infrastructure and systems Heterogeneous computing for AI (over GPU, TPU, NPU, FPGA, ASIC accelerations) High-performance networking and communication for AI workloads Efficient inference systems and serving architectures Edge AI systems and collaborative cloud-edge intelligence Resource management and scheduling for AI clusters AI workload characterization and benchmarking Storage systems optimized for AI training and data pipelines MLOps, AIOps, and lifecycle management of AI models Security, privacy, and trust of AI models Federated and distributed AI systems System support for generative AI and large language models Co-design of AI algorithms and system architectures RF Computing, Next-Gen AIoT, and Embodied Intelligence Scope We are on the cusp of a paradigm shift in the Internet of Things (IoT). The field is evolving from simple connectivity to a sophisticated ecosystem of pervasive sensing, distributed intelligence, and physical interaction. This track seeks to explore the synergy between three transformative technologies: RF Computing, which enables battery-free and non-intrusive sensing; AIoT, which brings intelligence to the edge; and Embodied AI, which translates digital insights into physical action. This track invites researchers, practitioners, and industry experts to submit original contributions that bridge the gap between wireless signals, algorithmic intelligence, and robotic systems. We are particularly interested in works that demonstrate how RF signals can inform embodied agents, how AIoT architectures can support mobile robots, and how these systems can operate efficiently in real-world environments. Topics of Interest We solicit papers covering a broad range of topics related to this convergence. Topics of interest include, but are not limited to: RF Computing & Wireless Sensing Joint Communication and Sensing (JCAS) / ISAC WiFi, LoRa, and UWB Sensing (Gesture, Vital Signs, Occupancy) Backscatter Communication and Computational RFID RF-based Localization and Tracking for Autonomous Systems Energy Harvesting and Battery-free Computing Reconfigurable Intelligent Surfaces (RIS) for Sensing AIoT & Edge Intelligence TinyML and Efficient Neural Networks for Edge Devices Distributed Inference and Learning across IoT Clusters Cross-modal Learning (RF + Vision + Audio) Resource-constrained Learning for Embedded Systems Privacy-preserving AI in IoT (Federated Learning, Split Learning) Security in IoT systems Embodied AI & Robotics RF-Guided Navigation and SLAM (Simultaneous Localization and Mapping) Sim-to-Real Transfer for Wireless Robots Human-Robot Interaction via Wearables/RF Sensing Multi-agent Coordination in AIoT Environments Sensor Fusion for Embodied Agents (Vision-RF, Lidar-RF) Systems & Applications Smart Home, Smart Health, and Smart Factory Applications Testbeds, Datasets, and Evaluation Metrics for RF-AI Systems Hardware-Software Co-design for Sensing and Actuation Web3.0 Security and Privacy Scope Web3.0 represents the next phase of Internet evolution, characterized by emerging technologies such as blockchain, cryptographic verification, and programmable smart contracts. These technologies enable new economic models, enhance transparency and security, and support trustworthy digital interactions at scale. The integration of AI further improves usability and automation, accelerating the adoption of intelligent, data-driven services. Together, these advances position Web3.0 as a transformative paradigm for the digital future. The Web3.0 Security and Privacy track aims to bring together innovative research that safeguards the next generation of decentralized digital infrastructures. We welcome contributions that advance the cryptographic foundations, protocol robustness, and application-level security of Web3.0, spanning decentralized systems, smart contracts, DeFi ecosystems, DAOs, and cross-chain environments. This track seeks to foster interdisciplinary dialogue among researchers and practitioners in cryptography, system security, economics, privacy engineering, and trusted hardware, highlighting solutions that enhance resilience against emerging threats while preserving usability and compliance. Particular interest lies in privacy-preserving blockchain architectures, Layer-2 scalability, secure integration in critical industries, AI-driven security analytics, and empirical measurement of real-world Web3.0 risks. Our motivation is to bridge theoretical breakthroughs with practical deployment challenges, enabling trustworthy, transparent, and resilient Web3.0 systems that can support sustainable innovation across digital society. Topics of Interest Our track seeks original contributions in the following topical areas, plus others that are not explicitly listed but are closely related: Cryptographic foundations for Web3.0 Decentralized protocol security Decentralized application and smart contract security DeFi security and economic resilience Governance and DAO security Privacy-preserving blockchain systems Layer-2 and cross-chain security System security for decentralized infrastructure Trusted execution and hardware-assisted Web3.0 Secure Web3.0 integration in critical industries Web3.0 data analytics and forensics Usability, compliance, and human-centric privacy in Web3.0 AI and emerging technologies in Web3.0 security and privacy Web3.0 measurement and empirical studies Agentic Design for System and Network Scope Intelligent agents are rapidly reshaping how operating systems and networks are designed, controlled, and secured. Driven by breakthroughs in large language models, reinforcement learning, and multi-agent coordination, agents are increasingly capable of perceiving complex system states, reasoning over operational objectives, and executing adaptive actions across distributed infrastructures. From self-healing networks and autonomous traffic engineering to AI-driven security orchestration and intelligent resource scheduling, agent-based paradigms offer potential for modern systems and network management. The Agentic Design for System and Network track aims to bring together pioneering research at the intersection of intelligent agents and systems/network engineering. We welcome contributions that explore how autonomous and semi-autonomous agents can enhance the performance, reliability, security, and efficiency of computing systems and communication networks. This track seeks to foster interdisciplinary dialogue among researchers and practitioners in distributed systems, networking, artificial intelligence, and security, highlighting solutions that harness agent intelligence to address real-world operational challenges. Particular interest lies in LLM-driven network management, multi-agent collaboration for distributed systems, agent-based cyber defense, autonomous cloud orchestration, and empirical evaluation of agent deployments in production environments. Our motivation is to bridge the gap between agent intelligence and systems engineering, enabling robust, adaptive, and self-managing infrastructures that can meet the demands of an increasingly complex and dynamic digital landscape. Topics of Interest Our track seeks original contributions in the following topical areas, plus others that are not explicitly listed but are closely related: Agent for System LLM-based and foundation model agents for operating system management and automation Multi-agent coordination for distributed computing and parallel workload scheduling Autonomous resource provisioning and orchestration in cloud and edge environments Agent-driven fault detection and root cause analysis Reinforcement learning agents for adaptive performance optimization Agentic frameworks for testing, debugging, and program repair Agent for Network Intelligent agents for autonomous network configuration and management LLM-driven network traffic engineering, routing optimization, and congestion control Multi-agent systems for software-defined networking and network function virtualization Autonomous agents for next-generation network environments Agent-assisted network protocol design, verification, and simulation Agent for Cybersecurity LLM-based and agentic approaches for vulnerability discovery Autonomous agents for intrusion detection and threat hunting Agent-driven malware analysis, reverse engineering, and forensic investigation Adversarial robustness of system-oriented agents Privacy and ethical challenges in deploying agents for cybersecurity Edge Intelligence Scope With the rapid evolution of artificial intelligence (AI) and edge computing, Edge Intelligence (EI) has emerged as a transformative paradigm that shifts AI capabilities from centralized cloud data centers to the network edge—closer to end devices, sensors, and real-world scenarios. As the core driving force of the current AI era, EI is no longer a supplementary technology but the "first scene" for AI implementation, addressing critical demands for low latency, reduced bandwidth consumption, enhanced privacy protection, and efficient energy usage in diverse application domains. From high-performance edge AI chips and on-device large model deployment to embodied intelligence and edge-agent systems, EI is reshaping industries ranging from industrial manufacturing and autonomous vehicles to smart homes and healthcare. Despite remarkable advancements—such as the deployment of large language models (LLMs) on edge devices, the rise of high-efficiency edge SoCs, and the integration of EI with digital twin and robotic systems—significant challenges remain. These include optimizing resource-constrained edge inference, addressing hardware-software co-design complexities, ensuring security and privacy in distributed edge environments, resolving toolchain fragmentation, and bridging the gap between large foundation models and edge deployment feasibility. To accelerate the advancement and adoption of EI, we invite original, high-quality research papers that explore novel theories, methodologies, technologies, and applications in this dynamic field. This call for papers aims to provide a platform for researchers, engineers, and practitioners from academia and industry to share breakthroughs, exchange ideas, and shape the future of Edge Intelligence. Topics of Interest We welcome submissions on a wide range of topics related to Edge Intelligence, including but not limited to: Edge AI Model Optimization Quantization, pruning, distillation, and compression techniques for resource-constrained edge devices On-device deployment of LLMs, vision-language models, and multimodal models. Edge Hardware & Software Co-Design High-efficiency edge SoCs and NPUs Heterogeneous computing architectures Non-von Neumann paradigms for edge scenarios Edge-Cloud & Edge-Edge Collaboration Task offloading, model partitioning, and collaborative inference strategies Federated learning and decentralized training for edge networks Edge intelligence agents and their orchestration frameworks Security, Privacy & Trust in EI Privacy-preserving edge AI Secure aggregation, differential privacy, and zero-trust mechanisms for edge networks Defense against model stealing, data poisoning, and inference-side attacks EI System Innovations Edge AI operating systems (Agent OS) Real-time scheduling and resource management Energy-efficient computing for battery-powered edge devices Benchmarks for heterogeneous edge platforms Real-World EI Applications Industrial IoT and autonomous factories Autonomous vehicles and robotic systems Smart healthcare, smart cities, and consumer electronics AR/VR with edge intelligence support Emerging Trends in EI Physical AI Semantic communication integrated with edge intelligence 6G-edge AI convergence Edge AI for sustainability and green computing Intelligent Computing Scope The Intelligent Computing track at IEEE ICPADS 2026 focuses on the co-design of theoretical foundations and algorithmic innovations with parallel and distributed systems to enable scalable, efficient, and trustworthy learning and decision-making. The track emphasizes distributed training and inference, communication-efficient optimization, heterogeneous computing, and AI-driven system optimization across cloud, edge, and high-performance computing platforms. We welcome contributions that bridge theory and practice, including novel models, architectures, and applications that advance intelligent computing in large-scale, real-time, and resource-constrained environments. Research integrating intelligent computing models with parallel computing theory, distributed systems, and high-performance computing is strongly encouraged. Our goal is to collaborate intelligent computing technologies applied in the field of embodied robot, services robot, and other intelligent applications in which parallel and distributed systems communities to advance intelligent systems or devices. Topics of Interest Our track seeks original contributions in the following topical areas, plus others that are not explicitly listed but are closely related: Deep Learning models and applications Distributed and Parallel Machine Learning Scalable Distributed Training and Optimization Communication-efficient Distributed Optimization Foundation Models and Large-scale training in Distributed Environments Graph Neural Networks and Large-scale Graph Intelligence Spatio-temporal and Sequential Learning Models Federated and Privacy-preserving Distributed Learning Systems Edge-Cloud Collaborative Intelligence Systems AI for High-Performance Computing Intelligent Resource Scheduling and System Optimization Data-parallel and Model-parallel Computing Strategies Trustworthy and Robust AI in Distributed Systems Heterogeneous Computing for Intelligent Workloads (CPU/GPU/TPU) Efficient Distributed Inference and Serving Systems Energy-efficient AI in Distributed Environments Intelligent Computing Applications on Scalable Distributed Platforms Intelligent computing upgrading traditional industry systems
Last updated by Dou Sun on

Acceptance Ratio

Average acceptance rate: 29.7% over 2 years (2009–2012).

YearSubmittedAcceptedAccepted(%)
20122948729.6%
20093059129.8%

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