In today's DigiPath Digest, we delve into federated learning, a decentralized approach to AI training that preserves data privacy.
I discuss recent papers from PubMed and share my experiences experimenting with AI tools like Perplexity and Gemini for research efficiency.
You will also get updates on upcoming plans, including leveraging AI to share more podcasts with you.
Did I mention that this is the last livestream of the year as I head to Poland for Christmas? No More DigiPath Digests. We got to number 18 (I overestimated it a bit in the podcast), and you have been instrumental in continuing this series!
Big THANK YOU to all the digital Pathology #TRLBLZRS showing up every Friday morning for this!
Join me as we tackle the nuances of federated learning and its impact on healthcare and pathology.
00:00 Introduction and Greetings 00:18 Today's Topic: Federated Learning 00:57 AI Tools and Updates 04:39 Federated Learning in Detail 08:03 Challenges and Benefits of Federated Learning 11:21 Exploring More Papers and Future Plans 22:53 Wrapping Up and Final Thoughts
📝 Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine 🔗https://pubmed.ncbi.nlm.nih.gov/39610333/
📝 A review on federated learning in computational pathology 🔗https://pubmed.ncbi.nlm.nih.gov/39582895/
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Podden och tillhörande omslagsbild på den här sidan tillhör Aleksandra Zuraw, DVM, PhD. Innehållet i podden är skapat av Aleksandra Zuraw, DVM, PhD och inte av, eller tillsammans med, Poddtoppen.
