Cooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID Data

arXiv:2608.25794 · cs.LG, cs.DC, cs.SD, eess.AS, eess.SP · Submitted 2026-08-26 · Read on arXiv

cs.LG, cs.DC, cs.SD, eess.AS, eess.SP

Submitted: 2026-08-26

Updated: 2026-08-26

Code: https://github.com/NexuFed/pFedMARL

Terminology

• Aggregation — 20× in this paper · explained in 1 episode
• Reinforcement Learning — 14× in this paper · explained in 4 episodes
• Federated Learning — 11× in this paper · explained in 16 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Non-IID Data — 8× in this paper · explained in 2 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Deep Reinforcement Learning — 6× in this paper · explained in 3 episodes
• Multi-Agent Reinforcement Learning — 6× in this paper
• Personalization — 6× in this paper · explained in 2 episodes
• Label Skew — 5× in this paper
• Regularization — 4× in this paper · explained in 3 episodes
• Actor — 3× in this paper · explained in 3 episodes
• Cluster Skew — 3× in this paper
• Generalization — 3× in this paper · explained in 13 episodes
• Data Heterogeneity — 2× in this paper · explained in 3 episodes
• Deep Reinforcement Learning (DRL) — 2× in this paper · explained in 2 episodes
• Federated Learning (FL) — 2× in this paper · explained in 7 episodes
• Multi-Agent Reinforcement Learning (MARL) — 2× in this paper · explained in 4 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Weighting — 1× in this paper · explained in 1 episode
• Adversarial scenarios — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Quantity Skew — 1× in this paper
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Server-side Deep Reinforcement Learning — 1× in this paper

Sources

• Continuous control with deep reinforcement learning
• MAE-AST: Masked Autoencoding Audio Spectrogram Transformer
• Prioritized Experience Replay

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