SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM

arXiv:2609.29446 · cs.LG, cs.NI · Submitted 2026-09-24 · Read on arXiv

cs.LG, cs.NI

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Accuracy — 42× in this paper · explained in 1 episode
• Federated Learning — 12× in this paper · explained in 14 episodes
• Decentralized Learning — 9× in this paper · explained in 2 episodes
• Decentralized Federated Learning — 5× in this paper · explained in 2 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Differential Privacy — 4× in this paper · explained in 4 episodes
• Data Heterogeneity — 3× in this paper · explained in 3 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Query Complexity — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Convergence Guarantees — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Alternating Direction Method of Multipliers (ADMM) — 1× in this paper · explained in 2 episodes
• Differentially Private — 1× in this paper · explained in 3 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Communication-Efficient Distributed Learning with Differential Privacy

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