Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particularly in sensitive domains such as healthcare. In this context, the TRUSTroke project aims to leverage FL to assist clinicians in ischemic stroke prediction. This paper provides an overview of the TRUSTroke FL network infrastructure. The proposed architecture adopts a client-server model with a central Parameter Server (PS). We introduce a Docker-based design for the client nodes, offering a flexible solution for implementing FL processes in clinical settings. The impact of different communication protocols (HTTP or MQTT) on FL network operation is analyzed, with MQTT selected for its suitability in FL scenarios. A control plane to support the main operations required by FL processes is also proposed. The paper concludes with an analysis of security aspects of the FL architecture, addressing potential threats and proposing mitigation strategies to increase the trustworthiness level.

In recent years, the barriers to the development of Artificial Intelligence (AI) have been broken down with the rapid progress of ABC technologies in computing: AI, Big Data, and Cloud Computing, as well as the emergence of cost-effective specialized hardware [213] and software [98]. This has led to the world entering the third wave of AI development: Deep Learning [117]. The success of current data-driven AI relies on massive amounts of training data and follows a gather-and-analyze paradigm [233], which confronts with challenges of complying with rigorous data protection regulations such as OECD Privacy Guidelines [217] and General and Data Protection Regulation (GDPR) [223]. So although data-centric AI is currently mainstream paradigm, Federated Learning [132], a novel model-centric distributed collaborative training framework, is gaining popularity in both academia and industry for its advantages in complying with privacy regulations [219]. According to the definitions of IEEE Standard for Federated Machine Learning (FML, aka FL) [205], FL is a framework or system that enables multiple participants to collaboratively build and use machine learning models without disclosing the raw and private data owned by the participants while achieving good performance. For example, a typical workflow of FL systems is that the entity with modeling demand (aka FL server) first deploys the FL services and initializes the model training task, and then distributing this task to participants with training data (aka FL clients) for modeling [13]. Based on this workflow pattern, many FL frameworks have been derived with specialized improvements in communication [111, 161, 240], optimizaiton [107, 129, 133], robustness [44, 124, 198] and privacy [14, 32, 62]. While these fascinating improvements greatly enhance the utility of FL, they all follow a task-based interaction paradigm, in which an FL server dominates the cooperation between FL participants. In this narrow interpretation of FL, the data owner is treated more like a worker than a collaborator and performs training primarily for the benefit of the server's goals.

Federated Learning (FL) is a machine learning approach that allows for the training of a model across multiple decentralized devices or institutions, without the need to centralize the data on a single server. It has been used across several industries, from mobile device keyboards to autonomous vehicles to oil rigs. It is particularly useful in the healthcare industry, where sensitive patient data is involved and strict regulations need to be followed to protect the privacy of individuals. In this blog post, we will discuss some practical steps to implementing a federated learning project with healthcare data. First, it is important to understand the requirements and constraints of your project.

Federated learning, as a privacy-preserving collaborative machine learning paradigm, has been gaining more and more attention in the industry. With the huge rise in demand, there have been many federated learning platforms that allow federated participants to set up and build a federated model from scratch. However, exiting platforms are highly intrusive, complicated, and hard to integrate with built machine learning models. For many real-world businesses that already have mature serving models, existing federated learning platforms have high entry barriers and development costs. This paper presents a simple yet practical federated learning plug-in inspired by ensemble learning, dubbed WrapperFL, allowing participants to build/join a federated system with existing models at minimal costs. The WrapperFL works in a plug-and-play way by simply attaching to the input and output interfaces of an existing model, without the need of re-development, significantly reducing the overhead of manpower and resources. We verify our proposed method on diverse tasks under heterogeneous data distributions and heterogeneous models. The experimental results demonstrate that WrapperFL can be successfully applied to a wide range of applications under practical settings and improves the local model with federated learning at a low cost.

Academia and industry have developed several platforms to support the popular privacy-preserving distributed learning method -- Federated Learning (FL). However, these platforms are complex to use and require a deep understanding of FL, which imposes high barriers to entry for beginners, limits the productivity of data scientists, and compromises deployment efficiency. In this paper, we propose the first low-code FL platform, EasyFL, to enable users with various levels of expertise to experiment and prototype FL applications with little coding. We achieve this goal while ensuring great flexibility for customization by unifying simple API design, modular design, and granular training flow abstraction. With only a few lines of code, EasyFL empowers them with many out-of-the-box functionalities to accelerate experimentation and deployment. These practical functionalities are heterogeneity simulation, distributed training optimization, comprehensive tracking, and seamless deployment. They are proposed based on challenges identified in the proposed FL life cycle. Our implementations show that EasyFL requires only three lines of code to build a vanilla FL application, at least 10x lesser than other platforms. Besides, our evaluations demonstrate that EasyFL expedites training by 1.5x. It also improves the efficiency of experiments and deployment. We believe that EasyFL will increase the productivity of data scientists and democratize FL to wider audiences.