53 citations · 55 across the 5 of their papers we have counts for
5 papers
Agentic Federated Learning: The Future of Distributed Training Orchestration
Rafael O. Jarczewski, Gabriel U. Talasso, Leandro Villas +1
Although Federated Learning (FL) promises privacy and distributed collaboration, its effectiveness in real-world scenarios is often hampered by the stochastic heterogeneity of clie…
Task-Centric Personalized Federated Fine-Tuning of Language Models
Gabriel U. Talasso, Meghdad Kurmanji, Allan M. de Souza +2
Federated Learning (FL) has emerged as a promising technique for training language models on distributed and private datasets of diverse tasks. However, aggregating models trained…
Beyond Shortest Path: Agentic Vehicular Routing with Semantic Context
Carnot Braun, Rafael O. Jarczewski, Gabriel U. Talasso +2
Traditional vehicle routing systems efficiently optimize singular metrics like time or distance, and when considering multiple metrics, they need more processes to optimize . Howev…
Fast, Private, and Protected: Safeguarding Data Privacy and Defending Against Model Poisoning Attacks in Federated Learning
Nicolas Riccieri Gardin Assumpcao, Leandro Villas
Federated Learning (FL) is a distributed training paradigm wherein participants collaborate to build a global model while ensuring the privacy of the involved data, which remains s…
Adaptive Client Selection with Personalization for Communication Efficient Federated Learning
Allan M. de Souza, Filipe Maciel, Joahannes B. D. da Costa +4
Federated Learning (FL) is a distributed approach to collaboratively training machine learning models. FL requires a high level of communication between the devices and a central s…