Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges

In recent years, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the most common approach in the literature, where a central entity creates a global model. However, a centralized approach leads to increased latency due to bottlenecks, heightened vulnerability to system failures, and trustworthiness concerns affecting the entity responsible for the global model creation. Decentralized Federated Learning (DFL) emerged to address these concerns by promoting decentralized model aggregation and minimizing reliance on centralized architectures. However, despite the work done in DFL, the literature has not (i)…

• FS
  Fundación Séneca

  Award: 21629/FPI/21
• EC
  European Commission

  Awards: TED2021-129300B-I00, PID2021-122466OB-I00
• UZ
  Universität Zürich