How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?

arXiv:2412.07813 · cs.GT, cs.AI, cs.LG · Submitted 2024-12-10 · Read on arXiv

cs.GT, cs.AI, cs.LG

Submitted: 2024-12-10

Updated: 2026-09-22

Comments: 15 pages, 10 figures

DOI: 10.1109/TON.2026.3734988 10.1109/TON.2026.3734988

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Accuracy — 27× in this paper · explained in 1 episode
Federated Learning — 22× in this paper · explained in 15 episodes
Benchmark — 11× in this paper · explained in 2 episodes
Nash Equilibrium — 11× in this paper · explained in 2 episodes
Split Federated Learning — 11× in this paper
Stackelberg Equilibrium — 8× in this paper
Differential Privacy — 4× in this paper · explained in 4 episodes
Communication Overhead — 3× in this paper · explained in 2 episodes
Clustering — 2× in this paper · explained in 2 episodes
Consistency — 2× in this paper · explained in 3 episodes
Core — 2× in this paper · explained in 2 episodes
Adam — 1× in this paper · explained in 2 episodes
Artificial Intelligence — 1× in this paper
Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
Benchmarking — 1× in this paper · explained in 3 episodes
Data Augmentation — 1× in this paper · explained in 7 episodes
Decentralized Federated Learning — 1× in this paper · explained in 2 episodes
Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
Generalizability — 1× in this paper · explained in 6 episodes
Homogeneity — 1× in this paper · explained in 2 episodes
Mobile Edge Computing — 1× in this paper · explained in 2 episodes
Reinforcement Learning — 1× in this paper · explained in 4 episodes
Stochastic Gradient Descent (SGD) — 1× in this paper · explained in 5 episodes

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