{"id":15159389,"url":"https://github.com/sharma-anee/federated_learning","last_synced_at":"2026-01-19T09:02:28.150Z","repository":{"id":198164145,"uuid":"550044651","full_name":"sharma-anee/Federated_Learning","owner":"sharma-anee","description":"This repo is about federated learning implementation using FLOWER framework for beginners.","archived":false,"fork":false,"pushed_at":"2022-10-12T07:08:12.000Z","size":5,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-13T18:49:36.542Z","etag":null,"topics":["deep-learning","deeplearning-framework","federated-learning","federated-learning-examples","federated-learning-framework","mnist-dataset","tensorflow","tensorflow-examples"],"latest_commit_sha":null,"homepage":"","language":"Python","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/sharma-anee.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,"publiccode":null,"codemeta":null}},"created_at":"2022-10-12T05:51:33.000Z","updated_at":"2022-10-12T06:36:15.000Z","dependencies_parsed_at":null,"dependency_job_id":"812182f3-172d-4d80-a5cc-68774f291007","html_url":"https://github.com/sharma-anee/Federated_Learning","commit_stats":{"total_commits":3,"total_committers":1,"mean_commits":3.0,"dds":0.0,"last_synced_commit":"e62bc27a1cb7f339c5960ea0acfc21c750656db1"},"previous_names":["sharma-anee/federated_learning"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sharma-anee%2FFederated_Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sharma-anee%2FFederated_Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sharma-anee%2FFederated_Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sharma-anee%2FFederated_Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sharma-anee","download_url":"https://codeload.github.com/sharma-anee/Federated_Learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247687248,"owners_count":20979425,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["deep-learning","deeplearning-framework","federated-learning","federated-learning-examples","federated-learning-framework","mnist-dataset","tensorflow","tensorflow-examples"],"created_at":"2024-09-26T21:20:39.374Z","updated_at":"2026-01-19T09:02:28.143Z","avatar_url":"https://github.com/sharma-anee.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Federated_Learning implementaion using Flower(1.0.0). \n\nThis implementation is for beginners using the Flower framework. Here, the MNIST dataset and a simple Deep Learning framework is used.\nThe scenario in here includes a server and two clients.\nEach client having different distribution of data (Non-IID data).\nFedAvg algorithm is used as the aggregation method on the server side.\n\nClone this repo using : git clone https://github.com/sharma-anee/Federated_Learning.git or else copy these codes into your own python editor and save into three seperate files as named here.\n\nSteps to execute these codes:\n1. Open the command prompt, move into the exact directories wherever your files are and execute the server.py file using : python server.py.\n2. Open another command prompt (a new one), and execute the client1.py file using : python client1.py.\n3. Repeat the Step 2. using : python client2.py.\n4. Close the graphs generated by step 2 and step 3.\n5. You shall see the training and updated model parameters in each of the server and client command prompts window.\n\n\u003cimg width=\"960\" alt=\"server_output\" src=\"https://user-images.githubusercontent.com/46394220/195273133-27db6be4-8fa4-453e-ac98-dc2558df8d8a.png\"\u003e\n\u003cimg width=\"960\" alt=\"client1_output\" src=\"https://user-images.githubusercontent.com/46394220/195273461-a4961cfa-05bd-4866-bc41-5145c7bbd175.png\"\u003e\n\u003cimg width=\"960\" alt=\"client2_output\" src=\"https://user-images.githubusercontent.com/46394220/195273323-40baedf3-56a8-4d44-a7e0-bc4f8e969974.png\"\u003e\n\nNote:  Make sure you have a stable internet connection.\n\nTo understand how it actually works and basics of the mechanism, one must go through https://flower.dev/docs/.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsharma-anee%2Ffederated_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsharma-anee%2Ffederated_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsharma-anee%2Ffederated_learning/lists"}