Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 17·5 min read
Designing Sustainable Federated Learning as a Service using Neural Architecture Search
TL;DR
A Sustainable Federated Learning as a Service (SFLaaS) framework was developed to address carbon constraints in federated training. It optimizes consumer participation and model performance while adhering to sustainability requirements.
✦ Why It Matters
Implement SFLaaS to ensure your federated learning projects meet sustainability goals without sacrificing performance.
Key Takeaways
- Adopt SFLaaS to align with carbon constraints in federated learning.
- Utilize the consumer-level carbon feasibility estimation for architecture evaluation.
- Implement adaptive consumer scheduling to enhance participation.
- Leverage real-world datasets to validate your federated learning models.
How It Works
SFLaaS uses a requirement-driven search space that translates consumer sustainability profiles into feasible architecture options. It incorporates a carbon feasibility estimation mechanism to assess candidate architectures dynamically, ensuring they meet carbon constraints.
The scheduling strategy selects consumers based on their feasibility and allocates workloads to maintain participation and data coverage.