Federated Learning

‍Federated learning is a machine learning technique that trains an algorithm via multiple independent sessions, each using its own dataset. The data is distributed across multiple devices or nodes and does not need to be centralized.

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Federated Learning is an innovative machine learning approach that enables model training across decentralised devices or servers while keeping data localised. In this collaborative learning paradigm, models are trained locally on individual devices, and only the model updates, not the raw data, are shared and aggregated to improve the overall model.

According to a recent survey, Federated Learning is expected to grow at a CAGR of 54.4% from 2021 to 2028. These statistics highlight the importance of Federated Learning in today’s world, where data privacy and security are becoming increasingly important. Another study found that Federated Learning can reduce the amount of data needed to train a machine learning model by up to 90% .

Federated Learning has become increasingly important in recent years due to its ability to provide transparency and interpretability in machine learning models. Federated Learning can help to ensure that AI models are trustworthy, fair, and accountable, and can provide valuable insights and benefits in different domains and applications

What is the difference between federated learning and machine learning?

The fundamental difference lies in the data distribution and training approach. In traditional machine learning, data is centralised, and the model is trained on a consolidated dataset. In federated learning, data remains decentralised, and models are trained locally on each device or server, allowing for collaborative learning without sharing raw data.

What are the steps in federated learning?

  1. Initialization: A global model is initialised on a centralised server.
  2. Local Training: Models are trained on individual devices using local data.
  3. Model Update: Only model updates (gradients) are sent to the central server.
  4. Aggregation: The server aggregates model updates to improve the global model.
  5. Iteration: Steps 2-4 are repeated iteratively for model improvement.

Is federated learning useful?

Yes, federated learning offers several benefits:

  1. Privacy Preservation: Raw data remains on local devices.
  2. Reduced Communication Costs: Only model updates are transmitted.
  3. Decentralised Training: Collaboration without data centralization.

What are the three types of federated learning?

  1. Horizontal Federated Learning: Devices share the same type of data but from different sources.
  2. Vertical Federated Learning: Devices share different features of the same data.
  3. Federated Transfer Learning: Pre-trained models are fine-tuned locally on specific tasks.

Does ChatGPT use federated learning?

As of my last knowledge update in January 2022, ChatGPT and similar models developed by OpenAI do not use federated learning. These models are typically trained on centralised servers with a large dataset.

Where can we use federated learning?

Federated learning finds applications in various domains:

  1. Healthcare: Collaborative training on medical data without data sharing.
  2. Smart Devices: Enhancing predictive capabilities on IoT devices.
  3. Finance: Fraud detection models trained locally on individual banks' data.

Does Google use federated learning?

Yes, Google has employed federated learning in various applications. For instance, Gboard, Google's keyboard app, uses federated learning for improving next-word suggestions without compromising user privacy.

Who proposed federated learning?

Federated learning was proposed by Google researchers Jakub Konečný and H. Brendan McMahan in a research paper published in 2016 titled "Federated Learning: Strategies for Improving Communication Efficiency."

What are the limitations of federated learning?

  1. Communication Overhead: Transmitting model updates introduces communication costs.
  2. Heterogeneous Devices: Varying computation capabilities among devices can affect model training.
  3. Security Concerns: Protecting against model inversion and membership inference attacks.
  4. Non-IID Data: Performance may suffer if the data distribution among devices is non-identically and independently distributed.

Examples of federated learning

  1. Gboard by Google: Gboard employs federated learning to enhance next-word predictions on mobile keyboards while respecting user privacy.
  2. Apple's QuickType Keyboard: Apple uses federated learning to improve the autocorrect and suggestion features on its devices, allowing models to learn from user behavior locally.
  3. Samsung's Federated AI Technology: Samsung has integrated federated learning into its devices for tasks like predictive text input and other personalized services.

Related terms 

  1. Decentralized Machine Learning: A broader concept referring to machine learning models trained across multiple decentralized devices or servers.
  2. Privacy-Preserving Machine Learning: Techniques and approaches, including federated learning, that aim to train models without exposing sensitive raw data.


In conclusion, federated learning represents a transformative approach to machine learning that prioritizes privacy and decentralized model training. By allowing models to be trained across distributed devices without exchanging raw data, federated learning addresses privacy concerns while promoting collaborative and efficient model development. 

This emerging paradigm holds great promise for various industries, fostering advancements in AI while safeguarding sensitive information. However, challenges such as communication efficiency and model aggregation complexities need continued attention to fully unlock the potential of federated learning in shaping the future of decentralized and privacy-preserving machine learning.


  1. https://en.wikipedia.org/wiki/Federated_learning
  2. https://www.techopedia.com/definition/federated-learning 
  3. https://builtin.com/machine-learning/federated-learning 
  4. https://www.mdpi.com/2076-3417/12/18/9124 
  5. https://research.aimultiple.com/federated-learning/ 

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