Short papers
Accepted papers will be published in Springer Nature Proceedings; short papers and extended abstracts are welcome.
Machine Learning/CORE Unranked
SustainableML-Afrasia 2026
SustainableML-Afrasia: Efficient and Trustworthy Machine Learning for Resource-Constrained, Climate-Vulnerable Communities across Asia and Africa
SustainableML-Afrasia 2026 is a half-day workshop at ACML 2026 in Melbourne, Australia, focused on advancing efficient, federated, robust, and trustworthy machine learning for resource-constrained and climate-vulnerable communities in Asia and Africa, with Australia as a third collaborative community. The workshop emphasizes algorithmic innovations grounded in real-world constraints like intermittent connectivity, limited computational resources, and data scarcity.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Extended abstracts
Accepted papers will be published in Springer Nature Proceedings; short papers and extended abstracts are welcome.
Research areas in scope
01
Efficient & data-efficient ML
PruningQuantizationDistillationNeural architecture searchEfficient architecture designHardware-aware learningCoreset selectionFew-shot and low-label learning
02
Federated, distributed & continual learning
Learning over heterogeneous clients under communication and systems constraintsSplit computingCommunication-efficient federated optimizationContinual learning under concept drift
03
Robustness & distribution shift
Domain adaptation and generalization for regional data shiftFairness and robustness evaluation under non-IID and low-resource conditions
04
Low-resource & multilingual learning
Multilingual and low-literacy NLPParticipatory and community-in-the-loop data collectionLearning from noisy, weakly labeled, or incomplete data
05
Sustainable & energy-aware learning
Carbon/energy-aware training and inferenceOptimization for sustainable machine learning
06
Trustworthy ML for deployment
Privacy-preserving analyticsCross-border data governanceSecure and maintainable model updates
Policies worth checking twice
- Double-blind reviews per submission
- Submissions do not conflict with the ACML main track or other venues
- Accepted papers will be published in Springer Nature Proceedings
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026