Federated Learning
Federated learning is a machine learning approach where models are trained across decentralized devices without sharing raw data. It enables privacy-preserving AI by keeping data on local devices while aggregating model updates.
Understanding Federated Learning
Federated learning is a distributed training approach where multiple devices or institutions collaboratively train a machine learning model without sharing their raw data. Instead of centralizing data, each participant trains a local model on its own data and sends only model updates or gradients to a central server, which aggregates them into an improved global model. This preserves privacy and is particularly valuable in healthcare, where patient data cannot be shared, and in mobile applications where user data stays on-device for edge AI deployment. Google popularized federated learning for improving keyboard predictions on Android devices. Challenges include handling non-uniform data distributions, communication efficiency, and defending against adversarial participants. Federated learning aligns with growing regulatory requirements around data privacy and pairs well with techniques like differential privacy to provide formal privacy guarantees.
Category
Machine Learning
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related Machine Learning Terms
Accuracy
Accuracy is a metric that measures the proportion of correct predictions out of total predictions made by a model. While intuitive, accuracy can be misleading on imbalanced datasets where one class dominates.
Active Learning
Active learning is a machine learning approach where the model selectively queries an oracle (often a human) for labels on the most informative data points. This reduces the total amount of labeled data needed to train an accurate model.
Anomaly Detection
Anomaly detection is the identification of data points, events, or patterns that deviate significantly from expected behavior. AI-based anomaly detection is used in fraud prevention, cybersecurity, and industrial monitoring.
AutoML
Automated Machine Learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML democratizes AI by reducing the expertise required.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of training data and combines their predictions. Random Forest is the most well-known bagging-based algorithm.
Bayesian Network
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies using a directed acyclic graph. It enables reasoning under uncertainty and causal inference.
Bias-Variance Tradeoff
The bias-variance tradeoff is the fundamental tension in machine learning between model simplicity (high bias) and model flexibility (high variance). Optimal models balance underfitting and overfitting to generalize well to new data.
Binary Classification
Binary classification is a supervised learning task where the model assigns inputs to one of exactly two categories. Spam detection (spam vs. not spam) and medical diagnosis (positive vs. negative) are common examples.