Centralized vs Decentralized Federated Learning: A trade-off performance analysis
Researchers are exploring new methods to improve federated learning, a technique for training models across decentralized data sources while preserving privacy. One approach, "Choose Wisely and Privately," uses mutual information and a Potential Federation Loss to proactively select clients whose data maximizes utility and fairness before training begins. Another study introduces a lightweight geometric signal to detect atypical clients by measuring how their local training diverges from the global model's functional behavior. Additionally, new theoretical work establishes general lower bounds for differentially private federated learning protocols and analyzes the trade-offs between centralized and decentralized federated learning architectures. AI
IMPACT These advancements in federated learning could lead to more efficient and secure collaborative AI model training, particularly in scenarios with sensitive or distributed data.
- Fedstellar
- Chaimaa Medjadji
- MNIST
- Secure Cross-Cloud Federated Learning
- Federated Learning
- Decentralized Federated Learning
- MLP classifier
- Secure-CCFL
- IEEE
- CIFAR-10
- Konstantinos Ziliaskopoulos
- Maxime Haddouche
- Choose Wisely and Privately
- Potential Federation Loss
- Yicheng Li
- Adda Akram Bendoukha
- Cristian Pérez-Corral