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NirajB07/CAFR-VEC-Caching

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CAFR — Cooperative Caching in VEC via Async FL + MARL

Trinity College Dublin MSc Dissertation (2025–26)

This repository contains trained model weights for the CAFR framework — a cooperative content caching system for Vehicular Edge Computing (VEC) that combines Asynchronous Federated Learning (AFL) and Multi-Agent Deep Reinforcement Learning (MARL).

Models in this repo

File	Description
`fl_autoencoder.pt`	FL global AutoEncoder (3883→100→3883), trained over 30 async FL rounds on MovieLens-1M
`dqn_agent1_cs*.pt`	RSU1 Dueling DQN policy, trained for each cache size ∈ {50,100,150,200,250,300,350,400}
`dqn_agent2_cs*.pt`	RSU2 Dueling DQN policy (cooperative MARL partner to agent1)

System Architecture

—FL model: AutoEncoder trained on vehicle rating data to rank content popularity
—RL agents: Two cooperative Dueling DQNs (CTDE — centralised training, decentralised execution)
—Shared state: RSU1 cache ∥ RSU2 cache (normalised popularity ranks)
—Action space: {0=keep, 1=replace 5 items}
—Reward: joint delay-minimisation signal (incentivises cache diversity)

Key Results (epochs=30, local_ep=10)

Cache Size	MCAF Hit Rate	vs ε-Greedy	Request Delay
50	11.94%	+0.84pp	34.37ms
200	34.09%	+-1.11pp	30.33ms
400	52.44%	+-1.48pp	26.65ms

Dataset

MovieLens-1M (proxy for vehicular content requests): 3,883 movies · 6,040 users · mapped to 15 vehicles via clustering

Loading a model

python

import torch
from model import DuelingDQN

agent = DuelingDQN(input_dim=100, num_actions=2)  # cs=50: input=100
agent.load_state_dict(torch.load("dqn_agent1_cs50.pt", map_location="cpu"))
agent.eval()

Citation

Cooperative Caching in Vehicular Edge Computing via Asynchronous Federated Learning and Deep Reinforcement Learning, IEEE 2023. Extended by Niraj Bharambe, Trinity College Dublin, 2026.