Rhutvik Gawade's blog : Federated Learning in AI: Training Models Without Sharing Data
As artificial intelligence continues to expand and develop, the issue of data privacy has become increasingly important. While data is still a critical part of any modern AI system—the more data, the better the model performance—the traditional way of doing this has involved centralizing all of the data in one place, exposing users to potentially serious privacy risks. Enter federated learning, which allows for the training of machine learning models and the development of artificial intelligence across multiple decentralized devices or servers, that hold data samples locally, while notifying the central server of model performance updates, without ever sharing or exchanging the data.
Federated learning (FL) fundamentally questions the belief that to develop a high-quality machine learning model, all of the data must be pooled into a central repository. Rather than sharing data, federated learning allows data to stay on local devices (or servers), and model performance updates, rather than raw data, are shared with a central server. The updates are then aggregated together to improve the centralized, shared model (our AI model) while preserving user privacy. Federated learning is increasingly being adopted through applications in predictive keyboards, health diagnostics, and financial modeling, where confidentiality and privacy are crucial.
For those wanting to become experts in privacy-preserving AI systems, an Artificial Intelligence Course in Pune is an excellent start, with many programs teaching modules on federated learning, and hopefully, it addresses both the benefits of developing strong AI and protection of the user's data by doing under the same roof. This way the participant is not just the technical person, but they will also have their moral compass aligned in their AI practices.
One of the most exciting sectors with applications for federated learning is healthcare. When we think about patient data, it's one of the most sensitive data classes of all, security is guaranteed for patient data via things like HIPAA and GDPR.
In cooperation with hospitals and research institutions, the healthcare industry is beginning to work together to build more accurate diagnostic models for diseases by using FL, without exposing patient data. With FL, these complex models can be now trained locally on several hospitals' patient data and the resulting model parameters can be shared across the cohort hospitals to build diagnostic model systems that can detect diseases like cancer, diabetes, or COVID-19.
Another area is federated learning on mobile and edge computing devices. Every day, smartphones, smartwatches, or IoT devices generate massive amounts of data from the user. Federated learning can leverage all of this user data from these devices in model development and training - for example, personalized recommendations, activity recognition, etc. The cool thing about federated learning is that the sensitive information (text messages, voice recordings, location, etc.) do not leave the device while allowing this profusion of data to be used.
The recent surge in interest for federated learning has led to greater demand for individuals who can design and implement such architectures. An Artificial Intelligence Training in Pune typically includes a brief introduction of frameworks that ease federated learning project development and execution, such as TensorFlow Federated and PySyft. While the technicalities of the approaches are significant, it is the practical experience that allows students to experience challenges such as system heterogeneity, communication overhead, and update aggregation - and it is through this experience that they are job-ready when they move into the industry.
Despite the enormous advantages federated learning can provide to implementing models, there are also distinct technical difficulties. One well-known technical difficulty is the uncertainty of model convergence when data across devices is not identically distributed. Since both hardware and infra might differ from one device to another, ensuring acceptable model performance, while also maintaining computational efficiency, can be quite challenging. Moreover, we have not addressed the technical difficulties surrounding secure channels of communication and potential inference attacks, e.g., making sure that the model updates they share does not leak sensitive information unnecessarily, by adopting techniques to address this, (e.g., differential privacy, secure multi-party computation and so forth).
Education institutions are addressing these complexities in the field by providing students not only with theoretical understanding but applied learning as well. Artificial Intelligence Classes in Pune are designed to provide more than an introduction to algorithms and fundamentals of Artificial Intelligence; students will be exposed to secure AI practices, edge computing and federated optimization paradigms.
Industries such as banking, insurance, and e-commerce businesses are realizing the advantages that federated learning offers. Federal learning in the financial services industry allows banks to cooperate on training fraud detection models without compromising how they keep customer data private. E-commerce businesses can build better recommendations engines without compromising privacy safeguards across regions in their terms of use. These new legal improvements by both the financial and e-commerce services industries will make federated leaning the new norm for operational compliance and innovative practices.
In conclusion, federated learning supports how artificial intelligence models operate by making data privacy and safety considerations part of the project from the outset instead of a follow-up discussion when new operational complexities develop. It also builds organizational trust with users where they have assurance their privacy is respected while enabling the business to realize the value on the previously left aside data that was left at rest. As the adoption of the federated learning approach extends, the early adopters who have the knowledge and capabilities to maximize the technique will lead the pace for digital development of privacy first artificial intelligence.
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