Federated Learning for Secure, Scalable AI Across the Enterprise

White paper

Smarter AI, without giving up control

As enterprises scale AI initiatives, the challenge shifts from building isolated models to securely coordinating intelligence across silos, business units, and geographies. Traditional centralized training poses significant limitations – especially in regulated sectors where data cannot leave its origin.

Federated learning offers a new paradigm: train AI models collaboratively without moving raw data. This approach unlocks the full value of enterprise data assets while preserving privacy, meeting compliance standards, and reducing infrastructure costs. When integrated into intelligent document processing and classification pipelines, federated learning enables real-time, adaptive AI that continuously improves from distributed signals – without compromising control.

This is exactly what Andersen can provide you with as a vendor of engineering and Artificial Intelligence consulting services.

Business challenge

Enterprises operating in finance, insurance, and large-scale services increasingly face:

• Data residency and privacy constraints preventing centralized model training;
• Multiple business units or regional systems with valuable but isolated datasets;
• A need to standardize AI behavior across jurisdictions without creating a central point of failure;
• Growing demand for AI models that can adapt without manual retraining cycles.

Federated learning addresses these demands by enabling cross-system collaboration without violating data sovereignty or incurring excessive cloud compute costs.

Typical indicators that trigger change

Companies ready for transformation often report:

• Inability to share sensitive data across departments or borders;
• Rising operational cost of retraining and redeploying AI models;
• Siloed model drift and inconsistent performance across regions;
• Compliance friction due to centralized handling of PII or regulated records.

Solution overview

The landscape of modern machine learning is characterized by an escalating demand for both computational scale and stringent data privacy. The explosive growth of data volumes, coupled with the increasing complexity of deep learning models, necessitates distributed training approaches to achieve practical scalability and accelerate development cycles.

Simultaneously, the proliferation of sensitive data – transactions, financial information, or personally identifiable details – introduces significant privacy risks. This dual imperative creates a critical need for solutions that not only efficiently distribute computational workloads but also inherently safeguard data throughout the entire training lifecycle.

Modules

PyTorch ecosystem for scalable training

The PyTorch ecosystem provides a powerful suite of tools designed to simplify and scale deep learning model training, abstracting much of the underlying distributed computing complexity and reducing development costs.

Andersen's team uses modern, reliable frameworks to enable secure and scalable distributed training on sensitive data.

PyTorch Lightning provides a structured foundation for managing multi-GPU and multi-node training, while Fabric simplifies scaling by reducing the complexity of setting up distributed workflows.

Privacy-preserving distributed training

Privacy-preserving machine learning (PPML) encompasses a suite of techniques designed to enable the training of machine learning models without compromising the privacy of the underlying data.

These methods directly address concerns regarding data security, privacy violations, and regulatory compliance.

Tools like PyGrid and PySyft allow training on decentralized or encrypted datasets without exposing raw data, enabling full compliance with strict privacy requirements.

Architecture

The configuration process begins with node registration through PyGrid's authentication system, followed by model architecture distribution via PyTorch Lightning's automatic serialization.

Training coordinators can adjust privacy budgets, set convergence thresholds, and configure communication frequency through Lightning's built-in hyperparameter management. The system automatically handles node failures and dynamic scaling while maintaining privacy guarantees throughout the training lifecycle.

What partners gain

Unified AI capabilities across regions without moving sensitive data

Federated learning enables enterprise-wide intelligence by securely coordinating model training across jurisdictions and departments without violating data residency laws.

Unlocked hidden value from siloed datasets without compliance risk

Models learn from distributed data sources without exposing raw inputs, allowing financial institutions to maximize data utility while staying aligned with GDPR, PCI DSS, and internal audit requirements.

Streamlined AI operations across complex organizations

Federated workflows reduce the need for manual model updates and cross-departmental coordination, accelerating delivery while lowering the cost of ownership.

Improved AI accuracy with broader, real-world exposure

Distributed learning enables models to generalize from diverse environments and use cases, enhancing reliability across business lines and customer segments.

Regulatory-ready infrastructure for secure AI at scale

This approach supports zero-trust architectures and prepares organizations for evolving data governance standards without costly architectural overhauls.

Customer success story

Federated AI platform for a banking group

Challenge

A banking group operating across several regions faced critical challenges in developing robust AI models for fraud detection and risk assessment. Local regulations in each country prohibited the transfer of sensitive customer data beyond national borders, severely limiting the ability to centralize machine learning workflows. Multiple business units and subsidiaries maintained siloed datasets and inconsistent AI models, leading to duplicated efforts, increased compliance risks, and missed opportunities for cross-market insights. To solve these issues, they needed a scalable AI solution for their enterprise-level needs.

Business value

The company implemented a federated learning framework enabling the bank to train advanced AI models collaboratively – without moving or sharing raw customer data. By orchestrating distributed training across different business units and jurisdictions, the bank achieved the following measurable outcomes:

• 100% regulatory compliance with data residency and privacy laws in all operating regions;
• Several times faster model updates across business lines without manual retraining cycles;
• Reduction in operational costs related to data engineering and infrastructure;
• Enhanced fraud detection accuracy by aggregating learnings from diverse, real-world transaction data;
• Seamless collaboration between subsidiaries, unlocking group-level intelligence without compromising data sovereignty.

The federated AI platform, with secure AI agents for enterprise workflows, empowered the organization to standardize risk management, accelerate innovation, and confidently scale AI adoption across geographies while maintaining full control over sensitive information.

Conclusion

A new standard for secure, scalable AI for enterprises

Federated learning marks a pivotal shift in how enterprises approach AI at scale – moving from fragmented, compliance-constrained systems to secure, collaborative intelligence without compromise. In an era where data is both a strategic asset and a regulatory risk, organizations must find ways to unlock value without losing control. Federated learning delivers exactly that.

By embracing privacy-preserving distributed training and leveraging modern tools like PyTorch, PyGrid, and PySyft, enterprises can break down data silos, reduce operational complexity, and standardize AI behavior across geographies – without moving sensitive information or exposing themselves to compliance violations.

As digital transformation accelerates, organizations that adopt federated learning will not only reduce infrastructure costs and compliance friction, they'll establish a competitive advantage built on trust, security, and intelligent automation at scale. This is what Andersen can provide them with as a vendor of AI software development solutions.