Federated Learning
Federated Learning is a decentralized approach to machine learning where multiple devices collaboratively train a model without sharing their local data. Instead of sending raw data to a central server, each device trains the model locally and only shares updates (like model weights) with the central server. This method enhances privacy and security while leveraging distributed data sources, making it ideal for applications involving sensitive information or where data is distributed across many locations.
Related Conference of Federated Learning
October 28-29, 2025
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Federated Learning Conference Speakers
Recommended Sessions
- Federated Learning
- AI Ethics and Responsible AI
- AI in Cybersecurity
- AI in Finance
- AI in Healthcare
- AI-Powered Personalization
- Artificial Neural Network
- AutoML and Low-Code Machine Learning
- Causal Inference
- Data Democratization
- Data Privacy and Security
- Data Science
- Data-Centric AI
- Deep Learning
- Edge AI and IoT Analytics
- Generative AI
- Graph Neural Networks
- Large-Scale Data Engineering
- Multi-Modal Learning
- Quantum Machine Learning
- Real-Time Data Streaming
- Reinforcement Learning
- Synthetic Data
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