Centralized vs Decentralized Federated Learning: A trade-off performance analysis
Federated learning architectures face a critical design choice as IoT proliferation drives distributed training at scale. This comparative analysis of centralized, decentralized, and semi-decentralized FL approaches directly addresses a bottleneck for privacy-preserving ML deployment: which topology balances communication overhead, model convergence, and regulatory compliance. The findings matter for infrastructure teams building edge ML systems where data residency constraints make traditional centralized training infeasible, and for researchers optimizing FL frameworks under real-world resource constraints.
Modelwire context
ExplainerThe paper's contribution isn't proving one topology wins universally, but rather mapping which constraints favor which design. Most FL deployments assume centralized coordination is the default; this work quantifies when decentralized or semi-decentralized topologies actually reduce total cost (communication plus convergence time plus compliance overhead) despite higher per-round complexity.
This connects directly to the federated imputation work from May 15th, which tackled heterogeneous feature spaces across clients. That paper assumed a working FL infrastructure; this one examines the infrastructure choice itself. Together they address the two layers of real-world federated systems: topology (which architecture) and data alignment (what clients can actually train on). The neuromorphic chip paper from the same day also matters here, since edge devices running FL models face power budgets that make communication overhead a hard constraint, not just a performance metric.
If the authors release empirical benchmarks on real IoT networks (not simulated) showing semi-decentralized FL beats centralized on latency-constrained devices within the next six months, that signals the findings are production-ready. If the paper remains simulation-only or shows marginal gains under realistic heterogeneity, the topology choice remains deployment-specific rather than generally actionable.
Coverage we drew on
- Federated Imputation under Heterogeneous Feature Spaces · arXiv cs.LG
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MentionsFederated Learning · IoT · Centralized Federated Learning · Decentralized Federated Learning · Semi-decentralized Federated Learning
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