nevzatseferoglu/fl-service

This API caters to data scientists, simplifying remote host communication with service endpoints. It allows users to efficiently manage flower federated learning clusters.

fl-service

Presentation: https://www.youtube.com/watch?v=1xq5LQ0DfYg

Report: https://github.com/nevzatseferoglu/fl-service/blob/main/doc/Report.pdf

API which tailored to the needs of data scientists. The interface streamlines communication with remote hosts through the use of service endpoints, allowing for seamless API-based interactions. These endpoints empower users to initiate, terminate, and manage flower federated learning clusters, ensuring the preservation of a unified strategy across the system's architecture.

See: Command line client from here.

Docker installation, image generation and deployment

Generate SSH key-pair for the server

curl --location --request POST 'http://localhost:8000/ssh/generate_ssh_key_pair'

Register an host machine to the service

curl --location 'http://localhost:8000/ssh/copy-ssh-key-to-remote-host' \ --header 'Content-Type: application/json' \ --data-raw '{ "contact_info": "[email protected]", "ip_address": "192.168.1.105", "ssh_username": "username", "ssh_password": "password", "flower_type": "client", "fl_identifier": "test_model" }'

curl -X POST "http://localhost:8000/ssh/copy-ssh-key-to-remote-host" --header 'Content-Type: application/json' --data-raw '{ "contact_info": "[email protected]", "ip_address": "192.168.1.105", "ssh_username": "suav", "ssh_password": "abc123", "flower_type": "client", "fl_identifier": "test_model" }'

Upload source file to turn into docker image

• Request template;

curl -X POST "http://localhost:8000/docker/upload-source-files/ip_address/pytorch/" \ -H "Content-Type: multipart/form-data" \ -F file=@absolute_path_to_souce_zip_file

• Example;

curl -X POST "http://localhost:8000/docker/upload-source-files/192.168.1.105/pytorch/amd64/" -H "Content-Type: multipart/form-data" -F file=@/Users/nevzatseferoglu/Desktop/flower/example-project/project-source.zip

Starting deployment

• Request template;

curl -X POST "http://localhost:8000/docker/deploy/ip_address/"

• Example;

curl -X POST "http://localhost:8000/docker/deploy/192.168.1.105/"

Makefile commands

• make run Run the server on port :8000. It can be changed through makefile.

• make kill Kill the running server process.

• make lint Apply code formatting, importing sorting and linting.

Research

• Determine whether file locking is required.

Success Criteria

• Manuel installation versus, saas installation time improvement will be %70. (satisfied)
• At least 4 client + 1 server. (satisfied)
• Face detection algorithm will run at 80% accuracy and new data will improve to 85% percent.

ToDo

• Change the database in a way that it can manage more than one fl algorithm. That means there will be more than one (server/clients) configuration.
• Update architecture diagram
• The docstring for a function or method should summarize its behavior and document its arguments, return value(s), side effects, exceptions raised, and restrictions on when it can be called (all if applicable).
• Add diagrams (sequence)
• Set request path, query parameters validation.
• Introduce usa-case scenario and set up instructions of the host machine.
• Incrase observability.
• Add missing informations to the doc.
• Mention about contraints such as recorded host can participate a single federated learning.
• Target host machine might require key file to connect through paramiko.