FedPGT: Progressive Gradient Transmission for Vehicular Federated Learning over Time-Varying Channels

arXiv:2609.18089 · cs.LG, cs.IT, math.IT · Submitted 2026-09-16 · Read on arXiv

cs.LG, cs.IT, math.IT

Submitted: 2026-09-16

Updated: 2026-09-16

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

Terminology

• Federated Learning — 42× in this paper · explained in 15 episodes
• Aggregation — 10× in this paper · explained in 1 episode
• Accuracy — 8× in this paper · explained in 1 episode
• Loss Function — 7× in this paper · explained in 2 episodes
• Communication Overhead — 6× in this paper · explained in 2 episodes
• Quantization — 4× in this paper · explained in 12 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Objective Function — 3× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Data Heterogeneity — 2× in this paper · explained in 3 episodes
• Decentralized Federated Learning — 2× in this paper · explained in 2 episodes
• Joint Gradient Sparsification and Device Scheduling — 2× in this paper
• Differential Privacy — 1× in this paper · explained in 4 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Lyapunov function — 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
• Signal-to-noise ratio (SNR) — 1× in this paper · explained in 2 episodes
• Static Scheduling Algorithm — 1× in this paper

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