Authors: Aditi Ramanathan
Abstract: Federated learning (FL) has emerged as a transformative framework for building artificial intelligence (AI) models without directly sharing raw data among servers or organizations. Traditional cloud-based AI architectures rely on centralized data aggregation, where sensitive information is collected from multiple users and stored in one location for model training. This process, while effective in producing high-performance models, exposes critical vulnerabilities in data security, privacy, and ownership. Federated learning addresses these challenges through decentralized model training—allowing multiple devices or silos to collaboratively learn a shared model while keeping the raw data localized. Each participant trains the global model using its local dataset and transmits only model parameters or gradients to a central aggregator. This mechanism reduces the risk of data leakage or misuse and aligns with rising privacy regulations like GDPR and HIPAA. The approach is especially valuable in healthcare, finance, and telecommunications, where data privacy is not only ethical but legally enforced. Advances in encryption, secure aggregation, and differential privacy augment FL’s resilience against adversarial attacks. However, challenges still persist, including communication overhead, system heterogeneity, and the threat of malicious model updates. Integrating FL with cloud infrastructures introduces new paradigms for balancing computational efficiency and regulatory compliance. This synergy transforms traditional centralized machine learning pipelines into privacy-preserving distributed ecosystems. The evolution of FL also influences edge computing, enabling low-latency, privacy-aware learning closer to data sources. With ongoing research in adaptive aggregation protocols and homomorphic encryption, FL stands poised to redefine the standards of privacy-preserving AI. Its adoption marks a significant step toward responsible AI ecosystems where intelligence develops collaboratively without compromising the confidentiality of user data.
DOI: http://doi.org/10.5281/zenodo.17840325
