2.2.4. FL Local Operations enabler

2.2.4.1. Introduction 

One of key goals of FL is to assure protection of privacy of data, owned by individual stakeholders. Therefore, data is expected to be used only locally, to train local version of the shared model, and only parameters update proposals of the ML algorithm are shared with others. When the FL training process has concluded, the final shared ML model is used to deliver specific functionality, also called inference engine. Both operations (model training and model inference) involve access to private data. This means that it is crucial to “encapsulate” local processes within a single “node” (that is controlled by data owner). However, it should be noticed that the data that is being used in both FL training processes has to be in the same format, which is imposed by the ML model that is being employed. In order to carry out with all these local operations, the FL Local Operation enabler is proposed. It will consist of components: Local Data Transformer component (that will be in charge of guaranteeing that data is appropriately formatted for the FL model in use), Local Model Training component, Local Model Inference component, Communication component (to enable in and out communications between involved local parties and FL orchestrator and FL collector), Privacy Component (to provide privacy mechanisms for communication e.g. encryption).

2.2.4.2. Features 

Enabler embedded in each FL involved party performing local training.
Verification of local data formats compatibility with data formats required by FL.
Transformation of local data formats to format required by the ML system (possibly using predefined transformers).
The local results will be sent to the FL Training Collector in order to carry out the appropriate aggregation methodology over the common shared model.
Inference with the final shared ML model.
Communication of model updates via encryption mechanisms. A homomorphic encryptor will not permit outsiders to see the output model of each device/party (MITM attacks), whereas methods for creating differentially private noise will guarantee that Malicious Aggregator cannot be allowed to infer which records are actual models and which not.

2.2.4.3. Place in architecture 

FL Local Operations enabler is one of the Federated Learning enablers that together enable to deploy a federated learning environment. Functionally, it operates on scalability and manageability verticals in the Assist-IoT architecture.

More specifically the following figure provides the semantic diagam of the enabler:

Semantic Diagram of Fl Local Operations Enabler

2.2.4.4. User guide 

Interactions with this enabler are done through a REST API. In the FL environment the FL Orchestrator enabler sends appropriate configuration to FL Local Operations that later on communicate with FL Training Collector and FL Repository if required.

The enabler exposes REST API (see endpoints below) to communicate with external enablers/applications but also uses gRPC to communicate with FL Training Collector during model trainings.

Method	Endpoint	Description
POST	/job/config/{id}	Receive configuration for a training job
PUT	/model/{id}/{version }	Receive a new shared model
GET	/status	Get current status of the enabler
POST	/job/transformer/{id }	Receive any required data transformer for job with identifier id
POST	/predict/model/{id}/ {version}	Inference with model

2.2.4.5. Prerequisites 

The main prerequisities are the installation of Docker and docker-compose. These prerequisites are necessary in case of running the enabler as a container (Docker). However, it is also possible to run the component independently. In this case, it’s mandatory to have Python installed on the machine where the enabler will be executed. At least version 3.8 is recommended (this is the version of the Python image being used). It is also necessary to install some additional libraries or packages. These additional packages can be seen in the requirements.txt file (inside the application folder).

2.2.4.6. Installation 

The installation procedure for this enabler is under development and will be provided once the release of the enabler is completed.

2.2.4.7. Configuration options 

The configuration of the training process for the FL Local Operations enabler is done with a request to REST API where the following parameters for a training job to be executed can be set:

client_type_id - type of client indicating what mechanisms are used in it, e.g. “keras1”
server_address - address of FL Training Collector, e.g. “training_collector”
optimizer - name of the optimized to be used, e.g. “adam”
eval_metrics - list of evaluation metrics, e.g. [“MSE”]
eval_func - evaluation function, e.g. “Huber”
num_classes - number of classes, e.g. “10”
model_id - model identifier, e.g. “10”
model_version - model version, e.g. “10”
shape - shape of the data, e.g. [“32”, “32”, “3”]
config_id - identifier of the configuration to be used
batch_size - size of a batch, e.g. “64”
steps_per_epoch - number of steps in each epoch, e.g. “32”
epochs - number of epochs, e.g. “5”
learning_rate - tuning parameter in an optimization algorithm, e.g. “0.001”

2.2.4.8. Developer guide 

2.2.4.8.1. Components

2.2.4.8.1.1. Local model trainer

The Local Model Training component is responsible for local model training. During configuration it instantiates appropriate ML training libraries and, if this is the beginning of the process, initial version of the shared model. This step can be completed locally by the node owner, but this is unlikely. The main problem would be assuring uniformity of training methods across nodes belonging to different owner. More likely, the necessary modules (ML algorithm libraries and the initial version of the shared model) will be downloaded from the FL Repository.

2.2.4.8.1.2. Local model inferencer

The component is responsible for use of the trained model. Here, the model may be used: (1) after the FL process is completed, or (2) it may start to be used from a certain (predefined by the owner) level of quality of the shared model. In the latter case, each new version of the shared model would replace the previous one. Obviously, it is implicitly assumed that each new version of the shared global model will deliver better quality of results. Here, data to be fed into the trained model can be transformed using the Data Transformer component. Interpretation of the results of application of the model to specific input data (including actions to be, possibly, undertaken on the basis of the results) is likely to be provided by the data owner. However, it is also possible that appropriate module is going to be downloaded from the FL Repository.

2.2.4.8.1.3. Local communication

Responsible for communication between external entities and the enabler.

2.2.4.8.1.4. Data transformer

In IoT ecosystems, each partner may (and is likely to) store data in its own (private/local) format. Use of FL requires transformation of appropriate parts of local data into the correct format. This format has to be described as part of the FL configuration, and all participating nodes have to oblige. This may be achieved by node owner providing appropriate transformation component. However, such component can be envisioned as being downloaded from the FL Repository enabler.

2.2.4.8.1.5. Privacy

The component is not yet implemented and the description will be provided in the next release of the documentation.

2.2.4.8.2. Technologies

2.2.4.8.2.1. scikit-learn

A popular machine learning library often used for data preprocessing and transformation, for example encoding labels. It is open source and widely used in the industry.

2.2.4.8.2.2. pyTorch

An open source machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab (FAIR).

2.2.4.8.2.3. Python

Python is an interpreted high-level general-purpose programming language with a set of libraries. Very popular for data analysis and ML applications.

2.2.4.8.2.4. TensorFlow

A free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks.

2.2.4.8.2.5. Flower

A federated learning framework designed to work with a large number of clients. It is both compatible with a variety of ML frameworks and supports a wide range of devices.

2.2.4.8.2.6. OpenVINO

A free toolkit facilitating the optimization of a deep learning model. It is cross-platform and free to use.

2.2.4.8.2.7. OpenCV

A real-time computer vision library providing already optimized models. It is cross-platform and open-source.

2.2.4.8.2.8. Pailier Encryption, Affine Homomorphic Encryption

Two homomorphic encryption algorithms that will be used to preserve the privacy of the data without affecting the performance of the model. Component: Privacy

2.2.4.8.2.9. FastAPI

A popular web microframework written in Python, FastAPI is known for being both robust and high performing. It is based on OpenAPI (previously Swagger) standards.