Full Papers
Submitted for peer review; accepted papers are presented as oral presentations and posters at the workshop.
Machine Learning/CORE Unranked
DeCaF 2026
7th MICCAI Workshop on “Distributed, Collaborative and Federated Learning”
The 7th MICCAI Workshop on Distributed, Collaborative and Federated Learning (DeCaF 2026) aims to advance methodological research in federated, distributed, and collaborative learning for medical imaging and healthcare applications. It focuses on enabling robust, privacy-preserving model training across institutions without sharing raw data, addressing challenges like data heterogeneity, fairness, security, and real-world deployment of AI in clinical settings.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Research areas in scope
01
Federated, Distributed, and Collaborative Learning
Federated, distributed learning, and other forms of collaborative learningMulti-agent collaboration, orchestration, and agentic AI systems in healthcareFL techniques for efficient training/fine-tuning of large-scale foundation models (LLMs/VLMs)Collaborative inference strategies for distributed model deploymentOptimization, personalization, and fairness in heterogeneous FL environmentsImpact of data and compute resource heterogeneity in FLPrivacy-preserving techniques (e.g., secure aggregation, differential privacy) and cybersecurityAdvanced software platforms, benchmarking, and standardization for real-world FLApplications to multi-task learning, meta-learning, and rare disease analysisNovel medical datasets and benchmarks for distributed learning researchInteroperability initiatives across existing FL software librariesEfficient communication and learning (multi-device, multi-node)Adversarial, inversion and other forms of attacks on distributed, federated, and collaborative learningDealing with unbalanced (non-IID) data in federated and collaborative learningDiverse decentralized medical imaging data analysisSecurity-auditing system for federated learningAsynchronous learningSoftware tools and implementations of distributed, federated, and collaborative learningModel sharing techniques, sparse/partial learning of models
Policies worth checking twice
- Double-blind reviewing to avoid bias
- All deadlines are 23:59 Pacific Time
- Submissions must be made via the CMT system
- EDI (Equity, Diversity, and Inclusion) factors are considered in final acceptance decisions for similar-quality submissions
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026