116: DigiPath Digest #18 | Federated Learning in Pathology. Developing AI Models While Preserving Privacy

Digital Pathology Podcast

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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
Links and Resources:

• Subscribe to Digital Pathology Podcast on YouTube
• Free E-book "Pathology 101"
• YouTube (unedited) version of this episode
• Try Perplexity with my referral link
• My new page built with Perplexity

Publications Discussed Today:
📝 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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