Federated learning is a distributed machine learning approach that allows models to be trained on decentralized data sources while preserving data privacy. This technique is particularly useful for applications such as IoT and edge computing, where data is generated by a large number of devices in different locations. Federated learning enables real-time decision-making and more efficient data processing, making it an important tool in the era of big data and IoT.
The future of work is rapidly evolving with the introduction of new technologies such as AI, blockchain, and robotics. Remote work and the gig economy are becoming increasingly popular, and in-demand skills are shifting towards technical abilities like data analysis and cybersecurity. Soft skills such as communication and adaptability will remain essential, and upskilling will play a crucial role in staying competitive in the job market.
Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Robotics, Natural Language Processing (NLP), and Deep Learning are some of the most prominent buzzwords in the field of computer science and technology. Though often used interchangeably, these terms have distinct meanings and applications
