[D] Why is federated learning not more mainstream?

[D] Why is federated learning not more mainstream? - Reddit

The idea of being able to leverage the data of other companies/institutions for mutual gains seems like a very powerful concept.

Do you know why Federated Learning isn't more popular in ...

... not answer all the questions. Federated Learning offers several business benefits: Accelerated AI Innovation: By leveraging distributed data ...

Federated Learning - a tour of the problem, challenges ... - dataroots

Perhaps one of the most pressing ones is the lack of data privacy. Some businesses simply can't afford exposing their data to some remote location for model ...

A survey on federated learning: challenges and applications - PMC

Federated learning (FL) is a secure distributed machine learning paradigm that addresses the issue of data silos in building a joint model.

Challenges and Trends in Federated Learning for Well-being and ...

Recent studies provided evidence that prototypes trained by adopting Federated Learning strategies are able to achieve reliable performance, thus by generating ...

Privacy Attacks in Federated Learning | NIST

The challenge of memorized training data seems to be even worse for larger, more complex models - including popular large language models (LLMs) ...

Federated learning - Wikipedia

One of the primary defining characteristics of federated learning is data heterogeneity. Due to the decentralized nature of the clients' data, there is no ...

Federated Learning: How Private Is It Really?

Federated Learning (FL) is a widely popular structure that allows one to learn a Machine Learning (ML) model collaboratively.

Federated Learning: Challenges, Methods, and Future Directions

For instance, devices from remote areas may be more likely to drop due to poor network connections and thus the trained federated model will be biased towards ...

Using Federated Machine Learning to Overcome the AI Scale ...

The digital divide between big and small-data organizations is a serious concern due to self-reinforcing data network effects, where more data ...

Federated Learning for Healthcare Informatics - PMC

Federated learning is a paradigm with a recent surge in popularity as it holds great promise on learning with fragmented sensitive data. Instead of aggregating ...

A review of applications in federated learning - ScienceDirect

The emergence of this technology will solve the contradiction between data privacy and data sharing for dispersed devices. Due to the property that data are not ...

Can federated learning save the world? - University of Cambridge

And in training the speech recognition model, federated learning was more efficient than centralised training in any country. Such results ...

Know Your Data to Harness Federated Machine Learning

Real applications of federated learning are now rapidly emerging as organizations search for more data on which to train the AI systems they ...

Federated Learning in Julia - Machine Learning

Overall I feel like federated learning would be ideal if you have a very large existing userbase and you'd like to deploy some new machine ...

Federated Machine Learning: Revolutionizing Data Privacy - Serokell

It has significant implications for data privacy and security, as it allows sensitive data to be kept on local devices rather than being shared ...

Federated Learning: The Future of Distributed Machine Learning

For end devices which drop out in the middle of the federated learning process, their data cannot be fully utilized during the learning process.

A comparative study of federated learning methods for COVID-19 ...

FL can differ from centralized data sharing in a number of ways. While both approaches aim to optimize their learning objective, FL algorithms ...

Advances and Open Problems in Federated Learning - arXiv

powerful central server may not always be available or desirable in more collaborative learning scenarios. [459]. ... To accommodate a fraction d ...

A Review on Federated Learning and Machine Learning Approaches

FL introduces new avenues for AI research. FL is a revolutionary training strategy for developing tailored models that do not compromise user privacy. The ...