{"podcast":{"title":"The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations","slug":"the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831","podcast_index_feed_id":7871831,"rss_url":"https://feeds.fexingo.com/business/the-data-science-podcast.xml","website_url":"https://www.fexingo.com/","image_url":"https://audio.fexingo.com/business/the-data-science-podcast/cover.png","author":"Fexingo","episode_count":118,"summary":"Lucas and Luna sit at a data-science workstation, two thin laptops open to scatter plots and clustering visualizations, and ask: what can we actually learn from the numbers? Each episode of The Data Science Podcast with Fexingo is a grounded, specific conversation about a single analytics problem or machine-learning method — from regularization in regression to the bias-variance trade-off in random forests. Lucas leads with a journalistic eye for how models are built and tested in the real world, citing actual case studies like how Netflix used matrix factorization for recommendations or how healthcare researchers apply survival analysis to clinical trials. Luna keeps the discussion honest, asking about data quality, feature engineering pitfalls, and whether a model’s accuracy actually translates to business value. They never resort to buzzwords: instead, they walk through the workflow from data collection to deployment, discussing trade-offs like interpretability versus performance. The show serves data scientists, analysts, and engineers who want to stay sharp on methods without the hype. Listeners walk away with a clearer understanding of why one algorithm beats another on a gi…","last_synced_at":"2026-07-19T08:17:23.323447+00:00","page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831"},"episode":{"title":"How Data Scientists Use Federated Learning for Privacy-Preserving ML","slug":"how-data-scientists-use-federated-learning-for-privacy-preserving-ml","published_at":"2026-07-12T08:39:11+00:00","page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-federated-learning-for-privacy-preserving-ml","show_page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831","url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0105.mp3","audio_url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0105.mp3","summary":"Episode 105 dives into federated learning, the privacy-preserving technique that trains models across decentralized data without ever centralizing sensitive information. Lucas and Luna unpack a real-world case: how Apple uses federated learning to improve QuickType keyboard predictions on iPhones without sending your typing data to the cloud. They break down the key technical components — local model training, secure aggregation, and differential privacy — and explain the trade-offs: communication cost vs. accuracy, and the challenge of non-IID data across thousands of devices. The conversation also touches on Google's Gboard implementation and how healthcare researchers are exploring federated learning for multi-hospital models without sharing patient records. Listeners will walk away understanding both the mechanics and the real-world constraints of one of the most important privacy technologies in modern machine learning. #FederatedLearning #PrivacyPreservingML #Apple #QuickType #Gboard #Google #SecureAggregation #DifferentialPrivacy #EdgeComputing #HealthcareAI #DataPrivacy #MachineLearning #Tech #FexingoBusiness #BusinessPodcast #Technology #DataScience #AI Keep every episode free: buymeacoffee.com/fexingo","meta_description":"Episode 105 dives into federated learning, the privacy-preserving technique that trains models across decentralized data without ever centralizing sensiti…","key_points":[],"chapters":[],"topics":[],"duration_seconds":676,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/episodes/how-data-scientists-use-federated-learning-for-privacy-preserving-ml/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-federated-learning-for-privacy-preserving-ml.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}