Design a Federated ChatGPT for Enterprise Privacy

Problem Statement

Design a Federated ChatGPT platform for large enterprises that provides ChatGPT-like conversational AI capabilities while guaranteeing that no prompt, conversation log, internal document, or training gradient ever leaves the enterprise firewall. The system must support on-premises or private-cloud LLM deployment, federated model improvement across siloed departments without raw-data sharing, domain fine-tuning on proprietary corpora, and a full administrative console for usage governance.

This is not a wrapper around the OpenAI API. A wrapper sends every prompt to a third-party server and violates the core constraint. The design must treat the LLM as an on-premises asset: downloaded once as a set of signed weights, served locally, fine-tuned locally, and improved across departments through federated aggregation of model deltas rather than centralised data pooling.

Why This Problem Exists

Bloomberg reported that BloombergGPT, a 50-billion-parameter model trained on 363 billion tokens of proprietary financial text plus 345 billion tokens of public data, was built and hosted entirely in-house because financial regulations prohibit sending client-related queries to external inference endpoints. JPMorgan Chase deployed its IndexGPT and LLM Suite internally for document analysis across 50,000+ employees, explicitly blocking external API calls from the inference layer. Apple's Private Federated Learning system demonstrated that model improvements can be aggregated from millions of devices without any single training example leaving the device, using secure aggregation and differential privacy.

These three production systems establish that the problem is real, the scale is large, and the architectural pattern—local inference plus federated improvement—is proven.

The Four Architectural Planes

1. Inference Plane: On-prem GPU clusters running quantised LLM weights behind a prompt-routing gateway. Departments interact through a unified chat API. No request leaves the VPC.
2. Knowledge Plane: Retrieval-Augmented Generation (RAG) pipeline indexing internal wikis, policies, codebases, and support tickets into department-scoped vector stores.
3. Federation Plane: Federated learning orchestrator that distributes a base model, collects per-department LoRA adapters or gradient deltas, aggregates them with secure multi-party computation, and redistributes the improved global model.
4. Governance Plane: Admin console, audit logging, RBAC, usage quotas, PII scrubbing, compliance reporting, and model version registry.

A strong interview answer keeps these planes separate. It allows the inference plane to degrade gracefully when the federation plane is offline, and it prevents the governance plane from becoming a bottleneck on the hot inference path.

What Makes This Different From a Standard Chatbot

A consumer chatbot optimises for response quality and latency. An enterprise federated ChatGPT additionally optimises for:

• Data sovereignty: every byte of prompt, completion, and retrieved context stays inside the corporate network.
• Departmental isolation: Engineering's code-related queries must not leak into Legal's fine-tuning corpus.
• Model provenance: every inference must be traceable to an exact model checkpoint, LoRA adapter version, and knowledge-base snapshot.
• Regulatory auditability: GDPR, HIPAA, SOX, and sector-specific rules may require that conversation logs are retained, encrypted, and deletable on request.

The brief explicitly requires on-prem deployment, federated model updates without raw data, optional domain fine-tuning, and an admin console. Every one of these must be designed for, not mentioned in passing.