Federated learning, a novel technique in the field of artificial intelligence (AI), is changing the way we train, validate and deploy machine learning models. This method changes the paradigm from a centralised, privacy-compromising approach to a distributed, privacy-preserving one, tackling both efficiency and ethical concerns that have been long-standing, important topics in the AI community.
The traditional machine learning workflow is based on a centralised approach, where data is collated from various sources and processed on a single, powerful system. This process requires vast amounts of data to be sent to a central server, raising potential data privacy issues. Enter federated learning, an alternative approach that flips this concept on its head.
Federated learning is a machine learning approach that allows for the training of an algorithm across multiple decentralised devices or servers holding local data samples without exchanging them. Instead of transferring raw data to a central server, each device uses its data to train a model locally and then sends only the model updates (the learned parameters) back to a central server. This server then aggregates these updates to form an updated global model, with the process repeating until the model converges. The process ensures that the data never leaves its original location, remaining behind its firewall at all times and therefore providing a robust solution for data privacy. Bitfount is one of the companies leading this shift from centralised to federated machine learning, putting it at the forefront of tackling both efficiency and ethical concerns in the AI sphere. You can try Bitfount’s easy-to-use federated machine learning platform here.
Now, imagine the power of federated learning combined with the impact of large language models (LLMs) such as GPT-4. LLMs are transforming our world by enhancing natural language understanding and generation, powering chatbots, personal assistants, code writing and more. However, their requirement for colossal data sets to be effective has raised serious privacy concerns.
By applying federated learning to train LLMs, a global foundation model can be trained that inherits the linguistic competencies from many individual datasets while preserving data privacy. This federated LLM could produce far richer, diverse, and personalised language models, as each dataset contributes its unique ‘learnings’ to the global model.
There's more to federated learning than just data privacy. In the era of IoT and 5G, the federation provides a scalable and efficient way to train models on edge, reducing the need for data transfer and allowing for real-time local adaptations. This decentralisation could drastically reduce the latency and network load, making AI systems more responsive and adaptable to local conditions.
In conclusion, federated learning is reshaping the AI landscape, providing a new paradigm for training machine learning models that prioritise both data privacy and efficiency. Its intersection with LLMs opens up the possibility for highly sophisticated and personalised AI systems that respect user privacy.