The growing need for data privacy and security in machine learning has led to exploring novel approaches like federated learning (FL) that allow collaborative training on distributed datasets, offering a decentralized alternative to traditional data collection methods. A prime benefit of FL is its emphasis on privacy, enabling data to stay on local devices by moving models instead of data. Despite its pioneering nature, FL faces issues such as diversity in data types, model complexity, privacy concerns, and the need for efficient resource distribution. This paper illustrates an empirical analysis of these challenges within specially designed scenarios, each aimed at studying a specific problem. In particular, differently from existing literature, we isolate the issues that can arise in an FL framework to observe their nature without the interference of external factors.

Speech Emotion Recognition (SER) technology helps computers understand human emotions in speech, which fills a critical niche in advancing human–computer interaction and mental health diagnostics. The primary objective of this study is to enhance SER accuracy and generalization through innovative deep learning models. Despite its importance in various fields like human–computer interaction and mental health diagnosis, accurately identifying emotions from speech can be challenging due to differences in speakers, accents, and background noise. The work proposes two innovative deep learning models to improve SER accuracy: a CNN-LSTM model and an Attention-Enhanced CNN-LSTM model. These models were tested on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), collected between 2015 and 2018, which comprises 1440 audio files of male and female actors expressing eight emotions. Both models achieved impressive accuracy rates of over 96% in classifying emotions into eight categories. By comparing the CNN-LSTM and Attention-Enhanced CNN-LSTM models, this study offers comparative insights into modeling techniques, contributes to the development of more effective emotion recognition systems, and offers practical implications for real-time applications in healthcare and customer service.