{"data":{"slug":"ailabstw-harmonia","name":"harmonia","tagline":"Federated Learning Made Easy","github_url":"https://github.com/ailabstw/harmonia","owner":"ailabstw","repo":"harmonia","owner_avatar_url":"https://avatars.githubusercontent.com/u/28085328?v=4","primary_language":"Go","stars":17,"forks":14,"topics":[],"archived":false,"github_pushed_at":"2020-09-21T08:16:29+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/ailabstw-harmonia","markdown_url":"https://www.graphcanon.com/tools/ailabstw-harmonia.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/ailabstw-harmonia","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=ailabstw-harmonia","description":"Federated Learning Made Easy","homepage_url":null,"license":"MPL-2.0","open_issues":0,"watchers":5,"ai_summary":"Harmonia is an open-source project that includes systems/infrastructures and libraries to facilitate the adoption of federated learning for researches and production usage. It supports differential privacy modules and GitOps-inspired architecture.","readme_excerpt":"# Harmonia \nHarmonia is an open source project aiming at developing systems/infrastructures and libraries to ease the adoption of [federated learning](https://en.wikipedia.org/wiki/Federated_learning) (abbreviated to FL) for researches and production usage. It is named Harmonia, the Greek goddess of harmony, to reflect the spirit of federated learning; that is, multiple parities collaboratively build a ML model for the common good. \n\nThe first release includes Harmonia-operator SDK and differential privacy modules (https://github.com/ailabstw/blurnn). We welcome contributions of new aggregation algorithms, privacy mechanism, datasets, etc. Let's work together to flourish the growth of federated learning. \n\n# FL System Architecture\n<div align=\"center\"><img src=\"./assets/architecture.jpg\" style=\"width:75%\"></img></div>  \n\nThe design of the Harmonia system is inspired by [GitOps](https://www.weave.works/blog/gitops-operations-by-pull-request). GitOps is centerred around a git repository, which maintains the desired states in the production environment. An automated process makes the production environment match the described state in the repository. In Harmonia, training plans (or simply FL parameters), global models, and local models are kept in git repositories. Updates to these repositories trigger FL system state transitions. These automates the FL training processes. A participant in a federated training is composed of an `Operator` container and an `Application` container. An `Operator` container is in charge of maintaining the FL system states, and communicates with an `Application` container via gRPC. Local training and aggerator applications are encapsulated in `Application` containers. This container based architecture enables quick plug-in of existing ML workflows.\n\n# Documentation\n* [docs](docs/)\n    * [docs/get-started](docs/get-started): A step-by-step [example](examples/mnist) tutorial\n    * [docs/sdk](docs/sdk/): Detailed sdk document\n\n# Build\nTo build the Harmonia `Operator`,\n```bash\n$ make all\n```\nfor `harmonia/operator` image\n\n# Example\nSee `examples/mnist`\n\n# Get Started\nSee [docs/get-started](docs/get-started)","github_created_at":"2020-06-04T03:20:25+00:00","created_at":"2026-07-11T23:38:20.451521+00:00","updated_at":"2026-08-04T18:01:42.288533+00:00","categories":[{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"differential-privacy","name":"differential privacy"},{"slug":"federated-learning","name":"federated-learning"},{"slug":"gitops","name":"gitops"}],"trust":{"provenance":{"is_fork":false,"github_id":269250328,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-04T18:01:41.440Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":2143,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:38:23.372Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-04T18:01:41.955Z"},"languages":{"value":["go"],"source":"github.language","observed_at":"2026-08-04T18:01:41.955Z"},"license_spdx":{"value":"MPL-2.0","source":"github.license","observed_at":"2026-08-04T18:01:41.955Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When needing frameworks that incorporate differential privacy directly into federated learning processes","For environments preferring a container-based plug-in architecture to integrate existing ML workflows"],"when_not_to_use":["If GitOps-inspired workflows are not aligned with your team's operational practices","In scenarios where the use of Go is less preferred among development teams"],"source":"enrich:decision_facts","observed_at":"2026-07-17T02:08:27.458Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Harmonia supports federated learning with differential privacy modules and GitOps-inspired architecture, designed for both research and production usage."}]}}