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Federated AI vs Centralized AI
Federated AI trains across distributed nodes without centralizing raw data; centralized AI trains on consolidated lakes with simpler ops but higher data-movement and residency exposure.
| Dimension | Federated AI | Centralized AI |
|---|---|---|
| Data locality | Raw data stays on device/site | Raw data ingested to central store |
| Latency to inference | Edge-local models; low RTT | Cloud inference; network dependent |
| Compliance posture | Strong for residency / air-gap | Requires DLP, encryption, audit on lake |
| Model refresh | Async rounds; version skew possible | Single pipeline; atomic deploys |
| Debugging | Harder (distributed traces) | Easier (central logs/metrics) |
Bajpai Labs recommends federated or hybrid architectures when residency or edge SLAs block centralization; centralized stacks when the lake is already the system of record. Edge-Sync and Synth-Data bridge gaps in both models.