Our verdict
We have not reviewed EasyFL yet
No one at Enterprise DNA has checked this listing against its sources. The description comes from public listings gathered automatically. Any fact below shows where it came from and when it was last checked.
From the listing
About EasyFL
EasyFL is an open-source federated learning platform that simplifies the setup and execution of distributed machine learning experiments. It provides a unified interface for simulating federated training across multiple clients, supporting common aggregation algorithms and data partitioning strategies.
Best for
Researchers and students exploring federated learning concepts in a simulated setting
Use cases
- Simulating federated learning workflows with custom data splits
- Benchmarking aggregation algorithms like FedAvg or FedProx
- Prototyping privacy-preserving distributed ML models
Notes
25 stars on GitHub. Last updated 2023-08-23. Licensed Apache-2.0.
Pros
- Low barrier to entry with a straightforward API for federated learning
- Supports multiple aggregation strategies out of the box
- Active community with open-source codebase for customization
Cons
- Limited to simulation environments, not production-grade deployment
- Small community with only 25 GitHub stars, so limited support and documentation
- Lacks advanced features like secure aggregation or differential privacy
Key facts
What we have checked
- GitHub stars
- 26
Each value shows when we last confirmed it and where it came from. Values we have not confirmed are left out, never guessed.
Stars and usage
How EasyFL is growing
25 to 26
Sources
Where this comes from
- github api via nightly crawl, recovered from git history 20e874270887c1008f860ee0198e461a23c9e438Registry
A source shows what was published when we checked it. It does not certify security, performance or a working installation. Read our method.
Changelog
What changed
No changes recorded yet. Releases, price changes and deprecations appear here, with their dates, once we track them for this record.
Pairs with
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