IoT · Global library

Federated Learning for Edge Devices

Train ML models collaboratively across edge devices without centralizing sensitive data

CodexClaude CodeKimi Codeorchestrator-mcp

Best use case

Use Federated Learning for Edge Devices when you need to train ML models collaboratively across edge devices without centralizing sensitive data, especially when the work is driven by federated learning and privacy.

Trigger signals

federated learningprivacydistributededge trainingcollaborative

Validation hooks

privacy-checkconvergence-test

Install surface

Copy the exact command path you need.

Inspect

pip install "orchestrator-mcp[dashboard]"
orchestrator-mcp skills show federated-learning-for-edge-devices

Use

orchestrator-mcp skills export federated-learning-for-edge-devices --to ./skillforge-packs
# copy the exported pack into your preferred agent environment

Export

cp -R skills/federated-learning-for-edge-devices ./your-agent-skills/federated-learning-for-edge-devices
# or open skills/federated-learning-for-edge-devices/SKILL.md in a markdown-first client

File patterns

*federated*.{py}*fl*.{py}*privacy*.{py}*distributed*.{py}

Model preferences

claude-sonnet-4gpt-4oclaude-haiku

Related skills

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Apache-2.0 licensedUse it, fork it, redistribute it. No strings attached.Safety-first by designSecret redaction, release scanning, and no shell execution. Constraints are public.GitHub nativeEvery pack is a real folder. Fork, edit, PR. No hidden backend or proprietary pipeline.

Federated Learning for Edge Devices

Copy the exact command path you need.

Adjacent packs to compose next.

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