The project, led by researchers João Silva e Costa (GTA-PEE/COPPE-DEL/Poli, Federal University of Rio de Janeiro – UFRJ), Guilherme A. Thomaz, Ronaldo A. Ferreira, and Miguel Elias M. Campista (Facom, Federal University of Mato Grosso do Sul – UFMS), was presented at the SBRC 2026
FLeer2FLeer (F2F) is a web-based tool for orchestrating federated learning based on a P2P architecture. F2F introduces an indexer responsible for discovering, announcing, and enrolling clients in multiple federations, without interfering with the training process.
The tool provides a decentralized orchestration infrastructure for Federated Learning (FL), enabling the training of artificial intelligence models without compromising the privacy of the users who own the data.
The project, led by researchers João Silva e Costa (GTA-PEE/COPPE-DEL/Poli, Federal University of Rio de Janeiro – UFRJ), Guilherme A. Thomaz, Ronaldo A. Ferreira, and Miguel Elias M. Campista (Facom, Federal University of Mato Grosso do Sul – UFMS), was presented at the SBRC 2026 Tools Exhibition and is available in the symposium proceedings.
Federated Learning
This work relates to federated learning, an approach initially developed by Google to address a central challenge in training artificial intelligence models: the need to collect large volumes of user data, which raised privacy concerns.
Instead of centralizing user data on a single server to train models, the federated learning approach reverses this logic. The model is sent to the devices or organizations that hold the data, where training is performed locally. Subsequently, only updates to the trained models are sent back to the central server, which combines (“merges”) these contributions to produce an increasingly accurate and convergent global model. This process depends on the participation of multiple clients training the model in parallel.
The procedure is repeated until the model converges or a stopping criterion is met. Because this distributed training approach preserves user privacy, it is well-suited for situations where training requires sensitive user information, such as data from Internet of Things (IoT) systems, medical data, or data generated by mobile devices.
Peer-to-Peer
Much of the existing literature assumes that these training processes are already underway. However, there is little discussion of a practical question: How does a new participant join a federated training process? How can a company or a user who possesses data find a compatible training process and begin to contribute?
It is precisely this gap that the article seeks to address. The proposal allows a client—whether a company or an end user who owns the data—to identify federated learning models that align with its interests and participate in the corresponding training processes.
By contributing their own data, customers help improve the model and, at the same time, obtain a system that is better tailored to their specific needs. In addition, existing models can continue to be refined on an ongoing basis as new data is incorporated into the training process.
Proposal
This research proposes the development of a web-based tool inspired by P2P architecture, called FLeer2FLeer (F2F), for training federated models. F2F proposes a server that acts as an indexer for discovering and managing federations. Unlike approaches focused on complex changes to the algorithm, the tool prioritizes the user experience and the governance of multiple simultaneous model training sessions.
FLeer2FLeer is an innovative tool that combines federated learning and peer-to-peer architecture to optimize the training of artificial intelligence models. Unlike traditional methods that centralize private information, this solution allows data to remain with the users, promoting greater privacy and security during processing.
The system facilitates collaboration between companies and individuals, enabling interested parties to locate and contribute to specific training programs in a distributed manner. By decentralizing the computational effort, the platform helps reduce operational costs and makes the models more accurate and tailored to the participants’ actual needs. The central focus is on overcoming the challenges of practical implementation by efficiently connecting data owners to processes for orchestrating global models.
Although the system is not an open public service, it can be deployed within companies, allowing different organizations to conduct collaborative training on distributed data without the need to centralize it.
This approach also makes it possible to distribute the computational cost among participants, since processing is carried out in a decentralized manner. The main challenge now is to transform this solution into a technology that can be applied in real-world scenarios, expanding its adoption beyond the research environment.