4 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…
HR-Agents: Using Multiple LLM-based Agents to Improve Q&A about Brazilian Labor Legislation
Abriel K. Moraes, Gabriel S. M. Dias, Vitor L. Fabris +14
The Consolidation of Labor Laws (CLT) serves as the primary legal framework governing labor relations in Brazil, ensuring essential protections for workers. However, its complexity…
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…