Nov 15“Navigating the Diversity: Tackling Non-IID Challenges in the Future of Federated Learning”In Federated Learning (FL), the nature of the data distribution among the participating devices (or clients) plays a crucial role in the training process. Two common terms used to describe data distribution in the context of FL are IID (Independently and Identically Distributed) and Non-IID (Non-Independently and Identically Distributed). …Federated Learning2 min readFederated Learning2 min read
Nov 15Future of Federated LearningFederated Deep Learning (FDL) has the potential to benefit a variety of industries, particularly those that handle sensitive or private data, require personalized models, or operate in decentralized environments. Here are some industries that may find federated deep learning particularly valuable in the future: 1. Healthcare — Federated learning can…Federated Learning2 min readFederated Learning2 min read
Nov 15“Transforming Deep Learning: A Deep Dive into Federated Learning”Federated Deep Learning (FDL) is an extension of traditional deep learning that introduces a decentralized approach to model training. In standard deep learning, a centralized server typically aggregates and processes all the training data to update a global model. …Federated Learning2 min readFederated Learning2 min read
Nov 15What really happens in dropout layer?In a dropout layer, during training, a random fraction of the input units is set to zero at each update. This process helps prevent overfitting by adding noise to the output and reducing the reliance on specific neurons. …Dropout2 min readDropout2 min read
Oct 27Adversarial attackAn adversarial attack, in the context of machine learning and deep learning, refers to a deliberate and malicious attempt to manipulate or deceive a machine learning model by introducing specially crafted inputs or perturbations. …Adversarial Attack4 min readAdversarial Attack4 min read
Oct 27Adversarial Neural NetworkAn adversarial neural network, often referred to as an “adversarial network” or simply “GAN” (Generative Adversarial Network), is a type of artificial neural network architecture introduced by Ian Goodfellow and his colleagues in 2014. …Generative Adversarial2 min readGenerative Adversarial2 min read
Oct 27What is adversarial model?An adversarial model is a component of adversarial machine learning, a technique that involves training two neural networks, known as the generator and discriminator, in a competitive fashion. The generator creates data or makes predictions, while the discriminator evaluates that data or those predictions. …4 min read4 min read
Oct 27Audio Deep Fake Detection: Revealing the Sounds of DeceitIntroduction The art of deceit is evolving along with the digital era. The development of audio deepfake technology in recent years is one of the most alarming technological developments. These advanced algorithms have the ability to alter audio recordings with a level of realism never seen before, which raises grave worries…Deepfakes2 min readDeepfakes2 min read
Oct 27Mitigating Prompts Injection Attacks in ChatGPT: Safeguarding AI Conversations from Harmful ManipulationIn the context of ChatGPT, prompt injection attacks are attempts by malevolent individuals to trick the AI model into producing offensive or dangerous content. Despite being a strong language model developed by OpenAI, ChatGPT is not impervious to abuse. …Prompts2 min readPrompts2 min read
Oct 27The AI Era’s Prospects for Public Transportation: A Revolution in MobilityIntroduction A new age of innovation and change is being ushered in by artificial intelligence (AI) in a number of industries. This also applies to public transportation, which has long been an essential part of urban mobility. …2 min read2 min read