FL

FL trains a global model by aggregating gradient updates from local models on decentralized datasets, ensuring raw training data never leaves local devices ^{[103, 137]}. Its Parametric Update classification reflects that FL does execute GD and produce formal weight modifications—distinguishing it from pipeline-mediated data strategies—but the critical structural differentiator is Decentralized: no centralized training corpus exists. Each client computes gradients locally on its private data shard and transmits only model updates (weight deltas or gradients) to the server for aggregation; raw data never leaves the originating device. This decentralized data geometry is what justifies Privacy Preservation as a D2 (Goal): FL does not merely apply privacy as a constraint layered onto a task-training objective—privacy preservation is an intrinsic structural property of the data access pattern, not an add-on mechanism. The absence of centralized training data means that data-quality auditing, distributional characterization, and bias assessment must be delegated to client-local verification processes or statistical reporting protocols; the server cannot directly inspect the training corpus, making third-party data-provenance attestation mechanisms a system-engineering requirement rather than an optional audit supplement.