{"id":19811123,"url":"https://github.com/accenture/labs-federated-learning","last_synced_at":"2026-03-04T15:31:26.079Z","repository":{"id":141762275,"uuid":"258120731","full_name":"Accenture/Labs-Federated-Learning","owner":"Accenture","description":"Accenture Labs Federated Learning","archived":false,"fork":false,"pushed_at":"2024-03-13T10:14:51.000Z","size":10771,"stargazers_count":106,"open_issues_count":3,"forks_count":30,"subscribers_count":14,"default_branch":"landing_page","last_synced_at":"2026-01-29T19:51:45.679Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Accenture.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2020-04-23T06:57:45.000Z","updated_at":"2025-11-19T00:29:45.000Z","dependencies_parsed_at":"2024-03-13T11:44:24.846Z","dependency_job_id":null,"html_url":"https://github.com/Accenture/Labs-Federated-Learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Accenture/Labs-Federated-Learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Accenture%2FLabs-Federated-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Accenture%2FLabs-Federated-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Accenture%2FLabs-Federated-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Accenture%2FLabs-Federated-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Accenture","download_url":"https://codeload.github.com/Accenture/Labs-Federated-Learning/tar.gz/refs/heads/landing_page","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Accenture%2FLabs-Federated-Learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30084960,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-04T13:22:36.021Z","status":"ssl_error","status_checked_at":"2026-03-04T13:20:45.750Z","response_time":59,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-12T09:24:56.879Z","updated_at":"2026-03-04T15:31:26.053Z","avatar_url":"https://github.com/Accenture.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Accenture Labs Sophia Antipolis Publications and Associated Code on Federated Learning\n\n- Yann Fraboni, Richard Vidal, Marco Lorenzi. Free-rider Attacks on Model Aggregation in Federated Learning. *AISTATS 2021*\n    - The paper can be found [here](http://proceedings.mlr.press/v130/fraboni21a.html).\n    - The code can be found [here](https://github.com/Accenture/Labs-Federated-Learning/tree/free-rider_attacks).   \n- Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi. Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. *ICML 2021*\n    - The paper can be found [here](http://proceedings.mlr.press/v139/fraboni21a.html).\n    - The code can be found [here](https://github.com/Accenture/Labs-Federated-Learning/tree/clustered_sampling).  \n- Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi. A General Theory for Client Sampling in Federated Learning. *FL-IJCAI'22*\n    - The paper can be found [here](https://arxiv.org/abs/2107.12211).\n    - The code can be found [here](https://github.com/Accenture/Labs-Federated-Learning/tree/impact_client_sampling).\n- Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi. A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. *JMLR 2023*\n    - The paper can be found [here](https://arxiv.org/abs/2206.10189).\n    - The code can be found [here](https://github.com/Accenture/Labs-Federated-Learning/tree/asynchronous_FL).\n- Yann Fraboni, Martin Van Waerebeke, Richard Vidal, Laetitia Kameni, Kevin Scaman, Marco Lorenzi. Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization. Preprint.\n    - The paper can be found [here](https://arxiv.org/abs/2211.11656).\n    - The code can be found [here](https://github.com/Accenture/Labs-Federated-Learning/tree/SIFU).\n- Yann Fraboni, Lucia Innocenti,Michela Antonelli, Richard Vidal, Laetitia Kameni, Sebastien Ourselin, Marco Lorenzi. Validation of Federated Learning on Collaborative Prostate Segmentation. *MICCAI 2023 - DeCaF*\n    - The paper can be found [here]().\n    - The code can be found [here](https://github.com/Accenture/Labs-Federated-Learning/tree/FU_prostate_segmentation).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faccenture%2Flabs-federated-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faccenture%2Flabs-federated-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faccenture%2Flabs-federated-learning/lists"}