Subhanshu Mohan Guptablogs.subhanshumg.com·Nov 17, 2024Federated Learning for Distributed MLOps SecurityIntroduction As Machine Learning Operations (MLOps) scale across industries, safeguarding sensitive data while enabling distributed training becomes a significant challenge. Enter Federated Learning (FL) — a decentralized approach that trains models ...10 likes·42 readspysyft
Bhargav jyoti BoruahforMachine Learning Club, NIT Silcharml-club-nits.hashnode.dev·Oct 31, 2024Federated Learning: Privacy-Preserving AIIn recent years, along with the blooming of Machine Learning (ML)-based applications and services, ensuring data privacy and security have become a critical obligation. ML-based service providers not only confront with difficulties in collecting and ...74 readsAI
Venkat Rvenkatr.hashnode.dev·Jul 12, 2024Exploring Generative AI and Synthetic DataGenerative AI (GenAI) and synthetic data are revolutionizing the way we approach data generation and utilization, particularly in training AI models. This comprehensive blog post delves into the essence of GenAI, the applications and benefits of synt...Generative AI (GenAI)
Rashid Ul Haqrashid-ul-haq.hashnode.dev·Jul 4, 2024An Introduction to Federated Learning: Decentralized Data, Centralized IntelligenceIn many real-world applications, training machine learning models on client data is challenging due to data exchange issues and user privacy concerns. To address these problems, McMahan et al. introduced federated learning in 2016. Definition of fede...Privacy in AI: Federated Learningfederated learning
Spheron NetworkforSpheron's Blogblog.spheron.network·Jun 25, 2024How Federated Learning Enhances AI in Web3Federated learning involves a collaborative effort by various devices and systems to contribute to a unified learning process. This enhances model robustness and ensures data privacy without requiring centralization or disclosure of sensitive informa...84 readsAI (Artificial Intelligence)federated learning
BDL Fadoua refdaraf.hashnode.dev·Feb 10, 2024A small door to Federated Learning Approach: Decentralised Data.Intoduction: Deep learning methods require a huge amount of data for training, which has raised significant privacy concerns. This means that the data used for training may contain sensitive information that could be exploited by malicious actors. As...26 readsDecentralised Data
Islam Ahmede1250.hashnode.dev·Aug 9, 2023Encryption for Security in Federated LearningWhat is Encryption Encryption is a process of converting plain text into ciphertext using an encryption algorithm and a key. The ciphertext is unreadable by unauthorized parties without the key. Encryption is used to protect sensitive information, su...encryption
Islam Ahmede1250.hashnode.dev·Aug 7, 2023Noise Function for Privacy (Federated)In the context of privacy, a Noise Function refers to a technique used to introduce random or pseudo-random values into data in order to protect individuals' privacy while still allowing useful analysis. This concept is closely related to the broader...Deep Learningfederated learning
Islam Ahmede1250.hashnode.dev·Jul 14, 2023Federated LearningWhat is Federated Learning? Federated learning is a machine learning technique that trains an algorithm via multiple independent sessions, each using its own dataset. This approach stands in contrast to traditional centralized machine learning techni...Machine learningMachine Learning
Bitingo Josaphatbitingo-the-deep-neural-nets.hashnode.dev·Apr 4, 2023Federated Learning with Differential Privacy in Computer VisionFederated learning is a new paradigm in machine learning that allows the training of models on data from multiple sources without having to share the data. This is particularly important in scenarios where data privacy is a concern or where it is dif...38 likes·398 readsArtificial Intelligence
