Federated learning (often referred to as collaborative learning) is a decentralized approach to training machine learning models. It doesn't require an exchange of data from client devices to global servers. Instead, the raw data on edge devices is used to train the model locally, increasing data privacy.
Feb 3, 2023
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Federated learning
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Federated learning is a sub-field of machine learning focusing on settings in which multiple entities collaboratively train a model while ensuring that their data remains decentralized. This stands in contrast to machine learning settings in which... Wikipedia
Aug 24, 2022 · Federated learning is a way to train AI models without anyone seeing or touching your data, offering a way to unlock information to feed new AI ...
Federated learning is a sub-field of machine learning focusing on settings in which multiple entities collaboratively train a model while ensuring that ...
Oct 13, 2019 · Federated learning is a way to develop and validate AI models from diverse data sources while mitigating the risk of compromising data security ...
Federated learning and analytics come from a rich heritage of distributed optimization, machine learning and privacy research. They are inspired by many systems ...
3 days ago · Key Takeaways · Federated learning enables training machine learning models on decentralized data across devices without sharing raw data, ...
TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data.
Apr 6, 2017 · Federated Learning enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data on device, ...
Federated Learning in AI: How It Works, Benefits and Challenges
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Aug 28, 2023 · Federated learning in artificial intelligence refers to the practice of training AI models in multiple independent and decentralized ...
Federated learning is a new decentralized machine learning procedure to train machine learning models with multiple data providers.