Contributed papers are solicited describing original works in Artificial Intelligence, Parallel and Distributed Systems. Topics and technical areas of interest include but are not limited to the following:

Track 1: AI Foundations and System Integration	Track 2: AI for System Optimization	Track 3: Parallel Training Paradigms
Distributed Machine Learning Training	Intelligent Workload Scheduling	Data Parallelism Strategies
Federated and Decentralized Learning	Predictive Auto-Scaling Policies	Model Parallelism Approaches
Large Language Model Systems	Anomaly Detection in Distributed Nodes	Pipeline Parallelism Methods
System Integration Methodologies	Adaptive Load Balancing Algorithms	Hybrid Parallel Training Architectures
Generative AI on Distributed Platforms	Energy-Aware Resource Allocation	Gradient Synchronization Protocols
AI-Driven System Optimization	AI-Driven Network Traffic Routing	Communication-Efficient Distributed Optimization
Track 4: Distributed AI Systems	Track 5: Federated and Decentralized Learning	Track 6: Scalable Applications on Parallel Infrastructure
Parameter Server Architectures	Federated Averaging Algorithms	Large-Scale NLP Model Training
All-Reduce and Collective Communications	Privacy-Preserving Distributed Aggregation	Distributed Computer Vision Systems
Distributed Data Loading Pipelines	Asynchronous Federated Update Rules	Scientific AI on Supercomputing Clusters
Fault-Tolerant Elastic AI Training	Decentralized Peer-to-Peer Learning	Real-Time Distributed Recommendation Engines
Scalable Model Serving Infrastructures	Heterogeneous Client Management Schemes	Autonomous Driving with Edge-Cloud Coordination
Distributed Hyperparameter Tuning	Secure Gradient Exchange Protocols	Benchmarking Distributed AI Workloads