Future Advances in AI Technologies for Federated Learning

Federated learning: what is it?

With federated learning AI, you can train AI models and unlock data to feed new AI applications without anyone seeing or interacting with your data.
Data mountains of training samples, either scraped from the internet or provided by users in exchange for free music, email, and other perks, were used to create the recommendation engines, chatbots, and spam filters that have made artificial intelligence ubiquitous in modern life.

Many of these AI systems were trained on data that was gathered and processed in a single place. Nonetheless, a decentralized approach is becoming more prevalent in contemporary AI. On the edge, people are working together to train new AI models with data that never leaves your mobile device, laptop, or private server.

In order to meet a variety of new needs, federated learning AI models a new type of AI training are rapidly taking over the processing and storage of private data. By processing data at its source, federated learning also offers a way to access the raw data from sensors on satellites, bridges, factories, and an expanding number of smart devices on our bodies and in our homes.

In order to promote discussion and idea sharing for the advancement of this new field, IBM is co-organizing a federated learning session at this year's NeurIPS, the world's leading machine learning conference.

How Does the AI Model for Federated Learning Operate?

Federated learning enables numerous people to remotely share their data in order to collaboratively train a single deep learning model and make incremental improvements, much like a team report or presentation. Each participant downloads the model, which is frequently a foundation model that has already been trained, from a cloud datacenter.

They compress and encrypt the modified model configuration after training it on their private data. The model changes are decrypted, averaged, and then sent back to the cloud to be added to the centralized model. Until the model is fully trained, the collaborative training process continues iteration after iteration.

This distributed, decentralized training approach has three variants. In horizontal federated learning, the core model is trained on similar datasets. In vertical federated learning, the information is complementary; for example, a person's musical preferences can be inferred by combining their evaluations of films and literature.

Last but not least, federated transfer learning involves training a foundation model on a different dataset to perform a different task, like identifying cats, after it has already been taught to recognize vehicles. Baracaldo and her colleagues are now working on integrating foundation models into federated learning. Building an AI model to detect fraud and then using it for other purposes is one potential use case for banks.

Federated Learning Benefits

There are several obvious advantages to federated learning AI models, especially in situations where decentralized data processing and data privacy are essential. Here are several key advantages:

Enhanced Data Privacy

Federated learning prioritizes privacy by allowing model training on decentralized data sources without direct access to the raw data. This decentralized approach reduces the likelihood of data breaches by guaranteeing that sensitive or private information remains on local devices.

Improved Defense

Since sensitive data is processed and kept locally on many devices, it is less centrally situated. This structure lowers the risk of serious breaches in comparison to traditional centralized learning methods.

Utilizing Data Effectively

Instead of collecting data centrally, federated learning may use data from several devices or institutions to increase model performance and accuracy. Because of this, the model can learn from a vast dataset, which is not possible with traditional methods.

Reduced Costs for Data Transfer

By sharing only model modifications rather than raw data, federated learning reduces network stress and data transmission costs. This would be especially helpful for applications with bad connections or environments where bandwidth costs are a concern.

Faster Learning with Real-Time Information

With federated learning, models may be updated nearly instantaneously as data is generated on local devices. This responsiveness is advantageous for applications like smart devices or personalized recommendations where up-to-date knowledge is crucial.

Respect for the Data Regulations

Federated learning is ideally suited to adhere to data privacy laws and regulations such as the GDPR since the data is kept locally. This could lessen compliance issues for companies handling user data in regulated industries like banking or healthcare.

More Personalization

Federated learning allows models to be customized to local data patterns while protecting user privacy. This is quite helpful for applications like personalized health monitoring or tailored recommendations.

In conclusion

Overall, federated learning enables safe, privacy-conscious AI advancements that allow for effective data use without endangering user trust or regulatory compliance.