1.0.0

Exaflow 1.0.0 Release Notes

Project & Architecture

• Exareme2 → Exaflow rename:
  The project has been renamed to Exaflow, reflecting its evolution into an umbrella orchestration engine. Exaflow now orchestrates federated workflows across Exareme3 and Flower, clearly separating orchestration from execution and learning backends.

• Ecosystem restructuring:
  Established a modular architecture where:

  • Exaflow acts as the orchestration layer
  • Exareme3 provides the framework for federated learning with the aggregation server paradigm
  • Flower operates in parallel to exareme3 as a standalone framework

  This restructuring improves separation of concerns and long-term extensibility.

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Framework Migration (Exareme2 → Exareme3)

• Database engine migration:
  Deprecated MonetDB in favor of DuckDB. DuckDB simplifies deployment, reduces operational overhead, and provides better support for embedded analytical workloads in federated nodes.

• Messaging layer replacement:
  Removed Celery + RabbitMQ in favor of a gRPC-based communication layer. This introduces strongly typed APIs, lower latency communication, and a more maintainable distributed architecture.

• Federated topology redesign:
  Replaced the local-global paradigm with a dedicated aggregation server model. This clarifies node responsibilities, improves scalability, and aligns the platform with modern federated learning deployment patterns.

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Federated Algorithm Library

• Core algorithm extraction:
  Extracted all core federated algorithm logic into a standalone federated learning library. This decouples algorithm implementation from orchestration and execution layers.

• sklearn-style interface:
  The new federated library follows a scikit-learn–like API (fit, predict, transform), enabling:

  • Easier algorithm development
  • Cleaner testing and benchmarking
  • Reusability outside Exaflow
  • Independent versioning and evolution

• Improved modularity:
  The separation between orchestration, execution framework, and algorithm layer significantly improves maintainability and extensibility of the platform.

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