Federated Learning is an innovative approach to machine learning that promises a new era of privacy and security in the field. Unlike traditional machine learning methods where raw data is sent to a central server for training, federated learning brings the model to the data source itself, allowing it to learn from decentralized sources.

This method has emerged as a response to growing concerns about privacy and data security. With conventional machine learning approaches, sensitive user data must be transferred from devices or local servers to centralized databases for processing. This transfer exposes vulnerabilities that could potentially lead to breaches of confidential information.

In contrast, Federated Learning allows models to be trained directly on users’ devices or local servers without needing access to raw data. It works by sending copies of an initial model across various nodes (devices or servers) which then learn from their respective datasets and update the model locally. The updated models are then sent back where they are aggregated into a single, improved model.

This decentralized approach keeps sensitive information secure on local devices while still enabling machine learning algorithms’ access necessary insights for improvement. Moreover, this process reduces the amount of raw data transmitted over networks which can lower communication costs and decrease latency issues associated with large-scale data transfers.

Another advantage is its potential for inclusivity in AI development since it allows participation from diverse sources without compromising privacy norms or regulations. For instance, healthcare institutions can contribute patient records without revealing identifiable information thereby enriching medical research while preserving patient confidentiality.

However, federated learning also presents challenges such as managing device heterogeneity due to varying computational capabilities and maintaining consistency between different versions of models across multiple nodes – but these are active areas of research seeking robust solutions.

Furthermore, advanced techniques like Secure Aggregation and Differential Privacy have been introduced within federated architectures enhancing privacy preservation even more. Secure Aggregation ensures that updates received at the central server cannot be linked back individually thus providing another layer of anonymity while Differential Privacy adds statistical noise during aggregation process making it impossible to reverse-engineer the data.

In conclusion, Federated Learning is revolutionizing machine learning by offering a privacy-preserving alternative to traditional methods. As we continue to generate vast amounts of data daily, this technology could be instrumental in maintaining user trust and ensuring that AI development remains ethical, secure and inclusive. This new era of machine learning privacy not only holds immense potential for safeguarding personal information but also opens up previously inaccessible datasets for research without compromising on confidentiality.