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The Indian Air Force (IAF) has successfully executed its assigned tasks in Operation Sindoor, with precision and professionalism. Operations were conducted in a deliberate and discreet manner, aligned with National Objectives. Since the Operations are still ongoing, a detailed…
🚨 All media channels, digital platforms and individuals are advised to refrain from live coverage or real-time reporting of defence operations and movement of security forces - Ministry of Defence, GOI.
Ministry of Information and Broadcasting issues advisory to all Media channels to refrain from showing live coverage of defence operations and movement of security forces in the interest of national security.
Excited to announce that I’ll be participating in @perplexity_ai AI Business Fellowship to further develop my skills within AI.
Earned my certificate in Federated Learning through the #30DaysOfFLCode challenge! 🚀 Big thanks to the @openminedorg organizers and it's amazing community for driving the future of AI forward. Let’s keep pushing boundaries together! 🌟
Day 29 of #30DaysOfFLCode : Joined @openminedorg webinar Introduction to Privacy-Preserving Machine Learning - Overview of privacy-preserving machine learning techniques - Example applications & case studies - Best practices for protecting sensitive data during model training
Day 28 of #30DaysOfFLCode : Watched Scaling Federated Learning: Meta AI's Mike Rabbat explains how FL trains models across billions of devices while keeping data private. Innovations in personalization, efficiency, and privacy unlock AI at scale. youtube.com/watch?v=j5e7bZ…
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“Federated Learning at Scale” Prof. Mike Rabbat, Meta AI
Day 27 of #30DaysOfFLCode : Explored interesting blog from Google - Advances in private training for production on-device language models research.google/blog/advances-… and started with paper arxiv.org/pdf/2306.14793 to understand Differentially Private FL for Gboard Language Models
Day 26 of #30DaysOfFLCode : Explored a interesting blog post on Learning with Privacy at Scale machinelearning.apple.com/research/learn… Local differentially private algorithms: - Private Count Mean Sketch - Private Hadamard Count Mean Sketch - Private Sequence Fragment Puzzle
Day 25 of #30DaysOfFLCode : Getting started with Apple Workshop on Privacy-Preserving Machine Learning 2024 machinelearning.apple.com/updates/ppml-w… Watched the Private Federated Learning (PFL) framework video machinelearning.apple.com/video/pfl-fram…
Day 24 of #30DaysOfFLCode : Deep dived into the Differential Privacy tutorial by Katrina Ligett from California Institute of Technology simons.berkeley.edu/talks/tutorial… Slides: simons.berkeley.edu/sites/default/…
Day 23 of #30DaysOfFLCode : Wrapped up today Architectural Patterns for the Design of FL Systems paper arxiv.org/pdf/2101.02373 Covered Model aggregation patterns are: - Asynchronous aggregator - Decentralised aggregator - Hierarchical aggregator - Secure aggregator
Day 22 of #30DaysOfFLCode : Cont. with Architectural Patterns for the Design of FL Systems paper arxiv.org/pdf/2101.02373 Went through Model training patterns: - Multi-task model trainer - Heterogeneous data handler - Incentive registry
Day 21 of #30DaysOfFLCode : Continued with Architectural Patterns for the Design of FL Systems paper arxiv.org/pdf/2101.02373 Today I covered Model management patterns: - Message compressor - Model co-versioning registry - Model replacement trigger - Deployment selector
Day 20 of #30DaysOfFLCode : Getting started with Architectural Patterns for the Design of Federated Learning Systems paper arxiv.org/pdf/2101.02373 Today I covered Client management patterns: - Client registry - Client selector - Client cluster
Day 19 of #30DaysOfFLCode : Today I revisited the Federated Learning Tutorial from NeurIPS 2020 - such an insightful read! drive.google.com/file/d/1QGY2Zy… It's fascinating to see how decentralized AI training is evolving. Highly recommend checking it!
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