Federated Learning in Big Data Over Networks
Explore federated learning techniques for big data networks, focusing on distributed algorithms and privacy-preserving methods in large-scale data analysis.
Federated and Collaborative Learning - Session 1
Explore cutting-edge topics in federated learning, including data valuation, privacy, uncertainty quantification, and scaling to foundation models, presented by leading experts in the field.
Federated and Collaborative Learning
Explore federated and collaborative learning approaches with Katrina Ligett, delving into innovative techniques for privacy-preserving data analysis and machine learning.
Introduction to Federated Learning with Flower - A Hands-on Tutorial
Explore federated learning with Flower framework. Learn AI training across devices, enhancing privacy without centralizing data. Includes hands-on tutorial and insights into reshaping AI while protecting sensitive information.
Efficient Decentralized Federated Singular Vector Decomposition
Innovative system for efficient decentralized federated SVD, enhancing privacy and performance in distributed applications. Outperforms existing methods with significant speed improvements and larger throughput.
Medical Imaging with Azure Machine Learning
Explore Azure Machine Learning for medical imaging applications, covering data management, model development, deployment, and explainability in healthcare scenarios.
Edge-Native: The New Paradigm for Operating and Developing Edge Apps
Explore edge-native principles for developing and operating apps across distributed locations, addressing challenges like NAT, limited resources, and heterogeneous environments for efficient edge computing.
Building Edge AI Stack and AI-as-a-Service in Cloud Native Way
Explore edge AI stack and AI-as-a-Service in cloud-native environments, focusing on KubeEdge architecture, ML offloading, and edge-cloud collaborative techniques for improved performance and data privacy.
Towards Collaborative Decentralized Learning
Explore cutting-edge decentralized learning concepts, from federated systems to Byzantine-robust training, with insights into collaborative and personalized optimization approaches.
AI and Blockchain: The Emerging Duets in Data Storage and Processing
Explore how federated learning and blockchain technologies combine to solve AI data sharing challenges, enhance security, and optimize decentralized storage systems across industries.
The Power of Adaptivity in Representation Learning - From Meta-Learning to Federated Learning
Delve into advanced machine learning concepts exploring adaptivity in representation learning, focusing on meta-learning and federated learning approaches for model deployment in new environments.
FedOpt++ - Federation Beyond Uniform Client Selection, Optimization Beyond Simple Minimization
Delve into advanced federated learning concepts, exploring cyclic client participation patterns and minimax optimization for distributed machine learning applications and theoretical frameworks.
Heterogeneity-Aware Algorithms for Federated Learning and Distributed Optimization
Delve into advanced federated learning algorithms, focusing on heterogeneity challenges in distributed model training and innovative solutions for edge computing optimization.
FATE-LLM - Empowering Large Language Models with Federated Learning
Explore FATE-LLM, integrating federated learning with large language models to address data privacy concerns. Learn about its components, implementation on Kubernetes, and real-world applications.
AI on the Edge: Hardware-Software Co-Design for Graph Analytics and Neural Networks - Session 3
Explore cutting-edge research on hardware-software co-design for AI, including graph analytics, neural networks, object detection, and federated learning.
Federated Learning and Integration of Biomedical Data
Explore federated learning for secure biomedical data collaboration, integrating data harmonization and imputation techniques. Discover applications in neuroimaging tasks.
On-Device Training Under 256KB Memory
Discover cutting-edge techniques for on-device machine learning training with minimal memory requirements, presented by MIT's Song Han in this technical seminar on resource-efficient AI deployment.
Federated Automatic Differentiation - Seminar 96
Explore advanced concepts in federated automatic differentiation with Google researcher Zachary Charles, covering innovative approaches to distributed machine learning and privacy-preserving computation.
A Bayesian Approach for Personalized Federated Learning - Seminar 93
Discover how Bayesian approaches enhance personalized federated learning through innovative statistical methods and practical applications in distributed machine learning environments.
Leveraging Spatial and Temporal Correlations in Distributed Learning - Seminar 91
Explore advanced distributed learning techniques focusing on spatial and temporal correlations, with insights into FedAvg algorithm, client participation, and optimization strategies.
Federated Learning - Basics and Application to the Mobile Keyboard
Explore federated learning basics and its application to mobile keyboards, covering practical implementation and real-world use cases in this informative tech talk.