RayFed

A multiple parties joint, distributed execution engine based on Ray, to help build your own federated learning frameworks in minutes.

Features

• Ray Native Programming Pattern

  Let you write your federated and distributed computing applications like a single-machine program.

• Multiple Controller Execution Mode

  The RayFed job can be run in the single-controller mode for developing and debugging and the multiple-controller mode for production without code change.

• Very Restricted and Clear Data Perimeters

  Because of the PUSH-BASED data transferring mechanism and multiple controller execution mode, the data transmission authority is held by the data owner rather than the data demander.

• Very Large Scale Federated Computing and Training

  Powered by the scalabilities and the distributed abilities from Ray, large scale federated computing and training jobs are naturally supported.

Supported Ray Versions

RayFed Versions	ray-1.13.0	ray-2.0.0	ray-2.1.0	ray-2.2.0	ray-2.3.0
0.1.0	✅	✅	✅	✅	❌
0.2.0	not released	not released	not released	not released	not released

Installation

Install it from pypi.

pip install -U rayfed

Quick Start

This example shows how to do aggregation across two participators.

Step 1: save the code.

Save the code as a python file, e.g., test.py.

import sys

import fed


@fed.remote
class MyActor:
    def __init__(self, value):
        self.value = value

    def inc(self, num):
        self.value = self.value + num
        return self.value


@fed.remote
def aggregate(val1, val2):
    return val1 + val2


def main(party):
    cluster = {
        'alice': {'address': '127.0.0.1:11010'},
        'bob': {'address': '127.0.0.1:11011'},
    }
    fed.init(address='local', cluster=cluster, party=party)

    actor_alice = MyActor.party("alice").remote(1)
    actor_bob = MyActor.party("bob").remote(1)

    val_alice = actor_alice.inc.remote(1)
    val_bob = actor_bob.inc.remote(2)

    sum_val_obj = aggregate.party("bob").remote(val_alice, val_bob)
    result = fed.get(sum_val_obj)
    print(f"The result in party {party} is {result}")

    fed.shutdown()


if __name__ == "__main__":
    assert len(sys.argv) == 2, 'Please run this script with party.'
    main(sys.argv[1])

Step 2: run the code.

Open a terminal and run the code as alice. It's recommended to run the code with Ray TLS enabled (please refer to Ray TLS)

RAY_USE_TLS=1 \
RAY_TLS_SERVER_CERT='/path/to/the/server/cert/file' \
RAY_TLS_SERVER_KEY='/path/to/the/server/key/file' \
RAY_TLS_CA_CERT='/path/to/the/ca/cert/file' \
python test.py alice

In the mean time, open another terminal and run the code as bob.

RAY_USE_TLS=1 \
RAY_TLS_SERVER_CERT='/path/to/the/server/cert/file' \
RAY_TLS_SERVER_KEY='/path/to/the/server/key/file' \
RAY_TLS_CA_CERT='/path/to/the/ca/cert/file' \
python test.py bob

Then you will get The result in party alice is 5 on the first terminal screen and The result in party bob is 5 on the second terminal screen.

Running untrusted codes

As a general rule: Always execute untrusted codes inside a sandbox (e.g., nsjail).