- Published on January 25, 2022
- In AI Features
Council Post: Overcoming the cyclical challenge of data utility and data privacy through Federated Learning
One of the defining characteristics of federated learning is that it keeps raw data decentralised, train model decentralised and then aggregate. Unlike traditional data centre-based distributed learning settings where data is arbitrarily distributed and any node within the network can access the data, Federated Learning involves heterogeneous distributed data to help protect privacy.
Image by Overcoming the cyclical challenge of data utility and data privacy through Federated Learning
- By Aishwarya Srinivasan
The increased availability of data has transformed the way companies work. Building data-driven strategies has now become a key differentiator. Organisations leverage data from their platform and multiple outside sources to build models that make predictions and inferences.
When it comes to data, the issue of privacy cannot be neglected. Data privacy doesn't solely concern the direct extraction of information but also with the inferential compromise of personal or sensitive information. With increased accessibility to technology, democratisation of software, and more importantly, with rising cloud migrations, our digital footprints are no longer in our control.
As users do not have full transparency about how their data is being collected, how it will be used, what kind o
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Aishwarya Srinivasan
Aishwarya is an AI & ML Innovation Leader at IBM. She works cross-functionally with the product team, data science team and sales to research AI use-cases for clients by conducting discovery workshops and building assets to showcase the business value of the technology. Aishwarya has founded a nonprofit organisation Illuminate AI that aims to provide mentorship, career guidance, and educational support to thousands in the community. She is also a board member for nonprofit organisations like AI for Good Foundation and AI Education project.
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