---

name: federated-learning description: "Use FedML for federated-learning simulation, cross-silo/cross-device/cross-cloud roles, algorithm flows, and privacy/security/analytics variants." disable-model-invocation: true metadata: disco-role: operating parent_skill: "fedml" license: Apache 2.0

FedML Federated Learning

Use this sub-skill for FedML federated-learning workflows: single-process simulation, MPI/NCCL simulation, cross-silo roles, cross-device/cross-cloud roles, algorithm-flow APIs, and advanced privacy/security/federated-analytics examples.

Do not use this for

• Ordinary centralized or cross-cloud non-FL training: use ../distributed-training/SKILL.md.
• Package building or launch submission details: combine with ../launch-and-packaging/SKILL.md.
• Android or IoT clients: those stacks were excluded from verified runtime coverage.

Mode selection

User task	Preferred route
quick local FL example	single-process simulation (backend='sp')
multi-process local simulation	MPI only after host MPI and mpi4py are verified
GPU/multi-process simulation	NCCL only after CUDA/NCCL/process launch are verified
cross-silo server/client job	cross-silo role-specific entry points plus launch/package guidance
privacy/security/analytics variant	start from the corresponding python/examples/federate/* reference and keep compute/data constraints explicit

Core API pattern

import fedml

args = fedml.load_arguments(training_type="simulation", comm_backend="sp")
args = fedml.init(args)
device = fedml.device.get_device(args)
dataset, output_dim = fedml.data.load(args)
model = fedml.model.create(args, output_dim)
runner = fedml.FedMLRunner(args, device, dataset, model, client_trainer=..., server_aggregator=...)
runner.run()

Use sp for a first smoke unless the task explicitly requires MPI or NCCL.

Algorithm-flow helper

For a local structural check of FedML's algorithm-flow API without datasets or backend services, run from the root skill directory:

python sub-skills/federated-learning/scripts/local_algorithm_flow_smoke.py

This does not prove full training convergence; it proves a local executor/flow pattern and is safe offline.

Evidence anchors

• python/examples/federate/quick_start/parrot/ — quick-start SP simulation examples.
• python/examples/federate/flow/fedavg/ — algorithm-flow pattern.
• python/examples/federate/quick_start/octopus/ and beehive/ — cross-silo package examples.
• python/examples/federate/security/, privacy/, federated_analytics/ — advanced variants.
• python/tests/smoke_test/simulation_mpi/ — MPI smoke reference only; MPI was not verified in the prep environment.

Backend cautions

• MPI is optional/unverified unless the target environment has mpirun and mpi4py.
• NCCL requires a compatible GPU/CUDA/NCCL and multi-process launch setup.
• Cross-silo/cross-cloud workflows need consistent YAML, server/client roles, backend env, and often API credentials.
• Do not run host-mutating MPI setup scripts automatically.

Exit criteria

A federated-learning task is complete when the FL mode, backend, role split, data/model assumptions, and launch-vs-local decision are explicit, and any unverified MPI/NCCL/credential requirements are recorded rather than implied.