This repository contains the source code for running a privacy perserving federated learning simulator. The source code is currently set up for the configuration of three clients performing secure and differentially private federated learning using logistic regresion on the MNIST dataset. This library, however, is meant to be modified so as to simulate your own secure federated machine learning configuration. We hope that this simulation can help users decide whether it is beneficial for them to participate in differentially-private federated learning for a given differentially private algorithm.
UPDATE : Paper accepted at the 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT
Paper and Video Link : https://dl.acm.org/doi/10.1145/3340531.3412771
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
First, clone this repository locally. Then create a conda enviroment by running:
conda env create -f environment.yml -n YourEnvironmentName
Activate the new enviornment:
source activate YourEnvironmentName
To validate correct installation cd to src and run
python run_simulation.py
If you encounter any issues, please let us know so that we can help in getting the simulation up and running.
This library is intended to be modified as needed for your use case. We have provided a default config.py file as an example.
Some simulation behavior can easily be configured by changing the files in the config file. The file contains Boolean variables USE_SECURITY and USE_DP_PRIVACY to toggle security and differential privacy features. The security feature does not affect accuracy, however you can set USE_DP_PRIVACY to False if you want to see what the federated accuracy would be without differential privacy.
The default config.py file also has USING_CUMULATIVE set to True. What that means is that the dataset for a client on iteration i containts all of the datapoints in iteration i-1 as well as len_per_iteration new datapoints. As such, this flag also makes it so that each client trains its weights from scratch each iteration. Conversely, one can set the USING_CUMULATIVE flag to False, which will make the dataset non-cumulative and clients perform gradient descent from last iteration's federated weights.
Agent (defined in agent.py) is the base class for this simulation. It is not meant to be initialized directly, but subclassed to create an agent with whichever behavior you would like. We have provided two sample subclasses, ClientAgent and ServerAgent, which are both used in the sample simulation.
An instance of the ClientAgent class represents an entity that is training a machine learning model on the same task as the other client agents. The initialization arguments for a client are agent_number, train_datasets, evaluator, sensitivity. Client agents are named assigned an agent_number, which is then appended to the string client_agent to create their name. For example, in the example simulation there are three client agents named client_agent0, client_agent1, and client_agent2. When initialized, clients are also provided their dataset, which in the example is a pyspark dataframe. The client is also passed an evaluator which it will use in the simulation to evaluate its own weights and the federated weights. evaluatoris an instance of the ModelEvaluator class defined in utils/model_evaluator.py.
There are two important methods of ClientAgent that are invoked by the ServerAgent. The first is compute_weights, which is called every iteration and prompts the client to perform its machine learning task on its dataset for that iteration.
The second method is receive_weights which is called at the end of every iteration when the server has federated weights to return to the client.
An instance of the ServerAgent class represents a third-party entity that is responsible for invoking the simulation and corresponding with the client agents. It is possible to configure a simulation to use more than one ServerAgent, but the straightforward example in the respository currently only creates one instance. Initializaing a ServerAgent only requires the same default argument for initializing its superclass Agent: agent_number which should be 0 for the first server agent.
ServerAgent has one method: request_values. The request_values is called to signal the server agent to start requesting values from clients, thereby starting the online portion of the simulation. The only argument is iters which dictates how many iterations to run the simulation for. Note that if iters is too large, the client agents may run out of data. For the example shown in the repository, only set iters to be equal to or less than to the iterations argument in the Initializer __init__ method, since that is the method that creates the datasets and distributes them to the clients. If you wish to change the behavior of the simulation, request_values is a good place to start and subsequently add/modify any methods that are called in the ClientAgent class.
The request_values method first requests weights in parallel from the clients by calling their compute_weights method, averages them, and then returns them to the clients in parallel by calling their receive_weights method. In the current example, the client returns a message to the server agent through the receive_weights method indicating whether its weights have converged.
An instance of the Initializer class is used to initialize the agents and model evaluator. In addition, any offline stage logic, such as a diffie-helman key exchange, should occur in this class. In our example, it loads the MNIST dataset and processes it for the client agent instances.
To commence the simulation, the initializer's run_simulation method is invoked, which then invokes the server agent's request_values method.
The Directory class contains a mapping of agent names to agent instances that allows agents to invoke other agents' methods by only having their name. An instance of Directory is created in the __init__ method of the Initializer class after all the agents have been created. It is then passed on to all the agents using their set_directory method.
An example usage to call some method of client_agent1 would be:
self.directory.clients['client_agent1'].METHOD_TO_CALL()'
This folder contains utilities such as data processing, differential privacy functions, and more. For the most part, functions in here are implementation specific and you should feel free to add any auxiliary functions scripts.
The library is intended to help users simulate secure federated learning to decide whether it would be feasible and beneficial. A snippet of a sample output looks like:
Performance Metrics for client_agent2 on iteration 1
-------------------------------------------
Personal accuracy: 0.8283333333333334
Personal computation time: 0:00:01.194242
Federated accuracy: 0.8566666666666667
Simulated time to receive federated weights: 0:00:07.202375
Performance Metrics for client_agent0 on iteration 1
-------------------------------------------
Personal accuracy: 0.8216666666666667
Personal computation time: 0:00:01.198737
Federated accuracy: 0.8566666666666667
Simulated time to receive federated weights: 0:00:09.202375
As you can see, the simulation prints out i) the personal accuracy: the accuracy that the client can obtain by itself on its own dataset, adding no DP noise. NOTE: this quantitiy does incorporate other client's data if you set the config.USING_CUMULATIVE flag to False, since that indicates to clients that they should start training on this iteration using the federated weights from the previous iteration since the datasets aren't cumulative. ii) the federated accuracy: the accuracy of the federated model which is the average of all the clients' personal weights + differentially private noise for that iteration. Note that while the clients benefit from participating in the simulation in this example, that is not always the case. In particular, as one increases the amount of differentially private noise, the federated accuracy is expected to decrease. On the other hand, the personal accuracy will remain the same since it is assumed you don't add differentially private noise to your personal model since you are not sharing it.
In addition, this library also allows you to simulate how long it would take to receive the federated values back for each iteration. Personal computation time indicates how long your training took for that iteration while Simulated time to receive federated weights takes into account user-defined communication latencies between the clients and the server, as well as how long it took the other clients to compute their weights and the server to average them.
Vaikkunth Mugunthan* Anton Peraire* Lalana Kagal
This project is licensed under the MIT License
MIT License
Copyright (c) 2020 Vaikkunth, Anton, Lalana (PrivacyFL)
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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