Combine model weights from sampled clients to update the global model. This process enables the global model to learn from each client's data.

A client is any machine with local data that connects to a server, trains on received global model weights, and sends back updated weights. Clients may also evaluate global model weights.

Docker is a containerization tool that allows for consistent and reliable deployment of applications across different environments.

Edge computing is a distributed computing concept of bringing compute and data storage as close as possible to the source of data generation and consumption by users.

Evaluation measures how well the trained model performs by testing it on each client's local data, providing insights into its generalizability across varied data sources.

Federated Learning is a machine learning approach where model training occurs on decentralized devices, preserving data privacy and leveraging local computations.

gRPC is an inter-process communication technology for building distributed apps. It allows developers to connect, invoke, operate, and debug apps as easily as making a local function call.

Inference is the phase in which a trained machine learning model applies its learned patterns to new, unseen data to make predictions or decisions.

The Internet of Things (IoT) refers to devices with sensors, software, and tech that connect and exchange data with other systems via the internet or communication networks.

Medical AI involves the application of artificial intelligence technologies to healthcare, enhancing diagnosis, treatment planning, and patient monitoring by analyzing complex medical data.

Model training is the process of teaching an algorithm to learn from data to make predictions or decisions.

The capability to run program across different hardware and operating systems.

Protocol Buffers, often abbreviated as Protobuf, are a language-neutral, platform-neutral, extensible mechanism for serializing structured data, similar to XML but smaller, faster, and simpler.

Scalability ensures systems grow with demand. In Federated Learning, it involves efficiently managing dynamic clients and diverse devices. Flower supports large-scale FL on various devices/ resources.

The central entity coordinating the aggregation of local model updates from multiple clients to build a comprehensive, privacy-preserving global model.

Vertical Federated Learning (VFL) enables collaborative model training across multiple organizations or institutions that share a common set of samples but hold different feature sets.

XGBoost - or eXtreme Gradient Boosting - is an open-source library providing a regularizing gradient boosting decisiong tree framework for many programming languages including Python, C++, and Java.