AI Assistant for Socioeconomic Empowerment Using Federated Learning (2025)

Abstract: Socioeconomic status (SES) reflects an individual’s standing in society, from a holistic set of factors including income, education level, and occupation. Identifying individuals in low-SES groups is crucial to ensuring they receive necessary support. However, many individuals may be hesitant to disclose their SES directly. This study introduces a federated learning-powered framework capable of verifying individuals’ SES levels through the analysis of their communications described in natural language. We propose to study language usage patterns among individuals from different SES groups using clustering and topic modeling techniques. An empirical study leveraging life narrative interviews demonstrates the effectiveness of our proposed approach.

## introduction socioeconomic status (ses) is a key determinant of an individual's opportunities, well-being, and access to essential resources such as education, healthcare, and employment. traditional ses assessments primarily rely on structured demographic data and self-reported surveys, which can be incomplete, biased, or intrusive. many individuals may be reluctant to disclose their ses due to privacy concerns or social stigma, further limiting the effectiveness of such assessments. recent advancements in natural language processing (nlp) provide an alternative approach by inferring ses from linguistic patterns in personal narratives. research has shown that differences in word choice, discourse structure, and emotional expression correlate with socioeconomic background. however, most prior work has relied on structured social media text or survey responses rather than free-form narratives, restricting the depth of analysis. this study introduces a federated learning-powered ses framework that preserves user privacy while analyzing life narratives. federated learning (fl) enables decentralized model training without exposing raw personal data, addressing critical privacy concerns associated with ses inference. our framework integrates nlp-based ses classification, topic modeling, clustering, and sentiment analysis to identify linguistic patterns linked to different ses levels. by leveraging a dataset of transcribed life narratives, we demonstrate that ses can be inferred effectively through language while maintaining privacy and scalability. to evaluate our approach, we test multiple machine learning and transformer-based models, with roberta after summarization achieving the highest performance. additionally, we assess the model's generalizability by testing it on out-ofdistribution (ood) data. our results highlight the potential of privacy-conscious ses classification for future applications in ai-driven social research and personalized support systems. the remainder of this paper is structured as follows. section 2 reviews prior research on identifying ses and section 3 presents our proposed federated learning framework, outlining its role in privacy-preserving ses identification. then section 4 describes the dataset and preparation steps and section 5 details our methodology, including the machine learning models used for ses classification, topic modeling and sentiment analysis. section 6 provides a broader discussion of the findings, their implications, and future research directions and section 7 summarizes the study's key contributions. finally, section 8 examines ethical considerations and societal impact. the paper ends with a section on the limitations of the research in section 9. ## related work understanding socioeconomic status (ses) through language has been explored in computational social science, sociolinguistics, and nlp. prior research has demonstrated that language use reflects socioeconomic differences, with variations in vocabulary, syntactic complexity, and discourse structures @xcite . lower-ses individuals tend to use more context-dependent language, while higher-ses individuals employ abstract and elaborative discourse @xcite . additionally, studies in psycholinguistics have shown that ses influences cognitive framing and emotional expression @xcite . traditional ses classification approaches rely on structured survey data or economic indicators such as income and education levels @xcite . computational methods have extended these approaches by analyzing text from social media to infer ses. for instance, @xcite classified twitter users' ses using gaussian processes, achieving 82% accuracy. similarly, @xcite trained random forest and xgboost classifiers on twitter data, reaching f1 scores of 0.70-0.73. other studies have used support vector machines (svms) and naïve bayes models to predict ses from online user profiles @xcite . however, these studies primarily rely on structured social media data or metadata rather than free-form personal narratives, which provide deeper insights into lived experiences. beyond social media, nlp techniques have been applied to infer ses-related attributes from diverse sources. @xcite predicted household ses from electricity consumption data, while @xcite used transit patterns to estimate ses indicators such as income. despite these advances, few studies have explored ses classification from life narratives, which contain richer self-reflections and personal challenges. privacy concerns in ses classification have led to the exploration of federated learning (fl) as a decentralized and privacy-preserving approach @xcite . fl has been widely applied in domains such as healthcare @xcite and finance @xcite , but its use in social science and ses inference remains limited. fl enables collaborative model training across decentralized devices with-out exposing user data, making it a promising solution for ses classification where individuals may be hesitant to share personal details. while prior studies have proposed fl for text classification tasks @xcite , this work is among the first to explore fl for ses inference from personal narratives. this study extends previous research by introducing a privacy-preserving ses framework that integrates fl with nlp-driven linguistic analysis. unlike prior works that rely on structured ses indicators, our approach analyzes life narratives using transformer-based models while maintaining data privacy. additionally, we evaluate our model on out-of-distribution (ood) data, addressing a major gap in ses classification generalizability. ## proposed framework traditional methods for socioeconomic status (ses) identification rely on centralized datasets and self-reported surveys, raising concerns about privacy, data availability, and scalability. this study introduces a federated learning-powered ses framework designed to preserve user privacy while allowing for decentralized model training. unlike conventional approaches that require users to share raw personal data, federated learning (fl) enables collaborative model refinement by exchanging only model updates. this approach reduces privacy risks while preserving the overall effectiveness of the process. this study implements and evaluates the ses classification and profiling stage within a simulated fl environment. the broader system envisions a privacy-preserving pipeline that integrates a knowledge graph (kg) to provide targeted recommendations based on ses profiling results. the focus of this study remains on demonstrating the feasibility of fl for ses classification and profiling, as well as assessing its generalizability. the proposed framework consists of three primary components. the first is the federated ses profiling system, which applies machine learning techniques to infer ses-related patterns from life narratives. this component has been developed and evaluated in this study, demonstrating the viability of fl-based ses profiling. the second component, a knowledge graph, is intended to enhance the system by mapping ses-related factors to relevant support resources. while not implemented in this study, it represents a future direction for generating personalized recommendations based on an individual's linguistic markers and sentiment insights. the third component involves local model refinement on client devices, enabling continuous personalization and adaptation without exposing sensitive user data. this final component remains conceptual and will be explored in future work. figure 1 provides an overview of the proposed federated learning-powered framework. the process begins with the deployment of the ses classifier to client devices, where users classify their personal narratives without transmitting raw text. a proposed extension of this process would involve generating ses profiles and using the knowledge graph to provide context-aware recommendations. users would then interact with the recommendations, offering feedback that refines the classifier, profiling system, and the recommendation system. the model updates generated from this interaction would be aggregated on a central server, improving classification and profiling system accuracy while preserving individual privacy. the simulated fl setup tested in this study captures only the model refinement process, demonstrating that ses classification and profiling can be performed in a decentralized environment without significant loss of accuracy. beyond classification, this study highlights the potential of integrating fl with ses profiling to support real-world ai-driven interventions. the next phase of this research will focus on refining model aggregation strategies, enhancing fairness in ses predictions, and developing an adaptive recommendation mechanism that aligns with users' socioeconomic contexts. as part of future work, we will explore real-world federated deployment and assess the effectiveness of ai-driven ses profiling in diverse settings. the federated learning approach offers several advantages for ses classification and profiling system. by keeping personal narratives on user devices, it eliminates ethical concerns related to direct ses data collection. the decentralized nature of the framework ensures that the system remains scalable and adaptable across different demographic groups. additionally, the classifier and profiling system continuously improves as more users contribute model updates, enhancing its ability to detect linguistic markers of ses over time. despite these benefits, several challenges remain before real-world deployment is feasible. sesrelated language varies significantly across individuals, introducing potential biases in model aggregation. the effectiveness of federated learning depends on user participation, as limited engagement in model fine-tuning could reduce the system's adaptability. moreover, integrating a knowledge graph for ses-driven recommendations requires further research to establish meaningful connections between classified ses categories and actionable support interventions. this study demonstrates the feasibility of federated ses classification and profiling system through simulation, highlighting its potential for privacypreserving nlp applications. the broader framework, including real-world federated deployment and a knowledge graph-driven recommendation system, remains a direction for future research. further exploration is needed to refine model aggregation strategies, improve fairness in predictions, and develop personalized recommendation mechanisms that align with users' socioeconomic contexts. ## empirical study this section details the methodology and results of two key analyses conducted in this study: ses prediction using machine learning and topic modeling for thematic exploration. the ses prediction task evaluates multiple classifiers, including traditional machine learning models and transformerbased approaches, to determine the most effective method for inferring ses from textual narratives. experimental results demonstrate that transformerbased models, particularly roberta after summarization, achieve the highest classification perfor-mance. in parallel, topic modeling is employed to uncover thematic patterns in the narratives across different ses groups. using a combination of embeddingbased clustering and sentiment analysis, we identify key topics related to socioeconomic experiences and examine their emotional tone. the results highlight both commonalities and distinctions in how different ses groups discuss various aspects of their lives. ## discussion and future work this study establishes a foundational step in developing a privacy-preserving ai framework for ses understanding. by leveraging nlp techniques, topic modeling, sentiment analysis, and federated learning (fl), we demonstrate that ses-related themes can be identified from personal narratives while ensuring data privacy. the classifier developed in this work is a key component of the broader system, validating the feasibility of text-based ses inference and its generalizability across different datasets. moving forward, we aim to implement the remaining components of the framework, extending beyond ses classification to a comprehensive profiling system. this system will integrate linguistic markers from the psychosocial model and sentiment analysis to create personalized insights and recommendations. by weighting these markers, the profiling system will adaptively assess an individual's ses-related discourse, providing a more nuanced understanding of their lived experiences. the next phase of development involves deploying the ses profiling system within a distributed fl environment, ensuring privacy while continuously improving model accuracy. additionally, we will develop a dynamic ses knowledge graph to map socioeconomic challenges, available resources, and intervention strategies. this knowledge-driven system will support an ai-powered recommendation mechanism, offering tailored financial, educational, and mental health support based on individual needs. beyond classification, this study highlights the potential for real-world applications of ses profiling in personalized ai-driven interventions. future work will focus on refining model aggregation strategies, enhancing fairness in predictions, and developing adaptive recommendation mechanisms that align with users' socioeconomic contexts. testing the complete system in diverse settings will be essential to assess its impact, ethical considerations, and effectiveness in supporting low-ses communities. ## contribution we have three major contributions. first, we proposed a novel framework that integrates federated learning (fl) with nlp-driven ses classification, allowing ses inference from life narratives while preserving data privacy. second, we conducted extensive experiments evaluating ses classification using both traditional machine learning and transformer-based models. additionally, we assessed generalization through out-of-distribution (ood) evaluations on unseen narratives. finally, we introduced a topic modeling approach based on social and psychological markers. ## ethical and societal impact first, while our data cannot be published or shared due to confidentiality agreements, we will release our trained model to enable others to classify ses levels from various types of life narratives. this approach ensures that the confidentiality of the dataset used in this study is maintained. the dataset itself was collected under an approved institutional review board (irb) protocol and has undergone thorough ethical review to ensure compliance with privacy and ethical standards. second, to mitigate potential misuse, such as using the model to infer ses from publicly available narratives for targeting individuals or groups for economic harm, we will release the model under a proper license and user agreement. this agreement will explicitly enforce compliance with legal and ethical standards, limiting the model's application to research and socially beneficial purposes. third, as part of our broader framework, we plan to integrate federated learning (fl), allowing decentralized model training while ensuring that personal data remains on user devices. finally, beyond privacy, this research aims to positively impact society by advancing the understanding of ses-related challenges. the ses profiling system, combined with a knowledge graph, can support ai-driven interventions in education, financial assistance, and mental health. future research will focus on transparency, ethical oversight, and collaboration with policymakers to ensure socially beneficial applications. ## limitations first, while our data-driven approach has achieved promising results, our analysis revealed that some misclassified samples showed a low distinction between the narratives of low, medium, and high ses classes. this suggests that certain narratives contain overlapping linguistic features that blur the boundaries between ses classifications. to address this, future work will explore incorporating a weighting system based on social markers to bet-ter differentiate ses classes within text narratives. second, although roberta with summarization provided the best performance, our findings indicate that summarization can lead to a loss of nuanced information. similarly, truncation and chunking approaches, while practical for handling lengthy narratives, lose different types of contextual data. in future studies, we plan to explore advanced context-preserving methods. finally, the private and sensitive nature of the data means it cannot be published or shared. however, we will make the trained model publicly available under a proper license to ensure its ethical use.