CCF: c CORE: b QUALIS: b1 Viewed: 245872 Tracked: 303 Attend: 92
Call For Papers
ICPADS 2026 (International Conference on Parallel and Distributed Systems) is a CCF C / CORE 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