---
# System prepended metadata

title: OVERVIEW

---

# OVERVIEW of paper about NON-IID in FL

1. **NON-IID types for FL and NON-IID solution survey**
    * [ZXL+21][Federated Learning on Non-IID Data: A Survey](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/rkxtkCFN6>)
    * [MZL+22][A state-of-the-art survey on solving non-IID data in Federated Learning](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/Hk5d8bsNp>)
    * [LDC+21][Federated Learning on Non-IID Data Silos: An Experimental Study](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/Hyp5hWjN6>)
    * [ZLL+22][Federated Learning with Non-IID Data](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/HkTey-iET>)　  <font color="#f00">important</font>
2. **FL client selection mechanism with NON-IID**
    1. **RL**
        * [AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/S14RrMsVp>)
        * [Optimizing Federated Learning on Non-IID Data ](<http://example.com/>) (not record)  <font color="#f00">important</font>
        * [Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered　Industrial IoT　 ](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/Hy8HcGiEa>)
    3. **stackberg + RL**
        * [Make Smart Decisions Faster: Deciding D2D Resource Allocation via Stackelberg Game Guided Multi- Agent Deep Reinforcement Learning](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/HJhmO-o46>)
    4. **others**
        * [Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/rJvsJGjNT>)
3. **NON-IID aggregation by determine weight**
    1. **RL** 
        * [FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/BJKsy3FSp>)
        * [ZLZ+23][Joint Device Scheduling and Bandwidth Allocation for Federated Learning over Wireless Networks](<https://>) <font color="#f00">再上傳筆記</font>
        * [ZLT+22][Deep reinforcement learning based scheduling strategy for federated learning in sensor-cloud systems](<https://>) (PPO) <font color="#f00">再上傳筆記</font>
    2. **Optimization**
        * [Personalized Cross-Silo Federated Learning on Non-IID Data](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/BJb99ljrT>)
4. **FL client selection mechanism wit IID**
    1. **RL**
        * [Applicability of Deep Reinforcement Learning for Efficient](<http://example.com/>) (not finish study)  <font color="#f00">important</font>
        * [Optimizing Federated Learning in Distributed Industrial IoT: A Multi-Agent Approach Federated　Learning in Massive IoT Communications ](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/H1Zfi-oNT>)
        * [Trust‑Augmented Deep Reinforcement Learning for Federated Learning Client Selection](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/rydV5HuB6>) (DQN)
        * [Experience-Driven Computational Resource Allocation of Federated Learning by Deep Reinforcement Learning](<https://>) (PPO) <font color="#f00">再上傳筆記</font>
        * [Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep  Reinforcement Learning Approach](<https://>) (DQN) <font color="#f00">再上傳筆記</font>
        * [Deep reinforcement learning based efficient access scheduling algorithm with an adaptive number of devices for federated learning IoT systems](<https://>) (ppo) <font color="#f00">未整理</font>
    2. **matching**
        * [Matching-Theory-Based Low-Latency Scheme for Multitask Federated Learning in MEC Networks](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/BkX7yZvST>) (multi-task)
    3. **coalition**
        * [Coalition based utility and efficiency optimization for multi-task federated learning in Internet of Vehicles](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/Bk7UvB7Lp>)
5. **Multimodel FL (MFL)**
    * [Multimodal Federated Learning: A Survey](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/rk9tWzj46>)
6. **Personalised FL**
    1. network design
        *  [Multi-Task Federated Learning for Personalised
Deep Neural Networks in Edge Computing](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/rky2lRSBa>)

7. **cluster FL**(之後可以再歸類到更詳細，基本上是)
    * [Federated learning with hierarchical clustering of local updates to improve training on non-IID data](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/rk8gWMUrp>) 
    * [Personalized Federated Learning Algorithm with Adaptive Clustering for Non-IID IoT Data Incorporating Multi-Task Learning and Neural Network Model Characteristics](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/HyAiFq_ST>)
8. **multitask FL**
    * [Joint Client Selection and Task Assignment for Multi-Task Federated Learning in MEC Networks](<https://>) <font color="#f00">再上傳筆記</font>
    * [Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks](<https://>) <font color="#f00">再上傳筆記</font>
    * [Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks](<https://>) <font color="#f00">再上傳筆記</font>
    * [Multi-Task Network Anomaly Detection using Federated Learning](<https://>) <font color="#f00">再上傳筆記</font>
    * [Multi-Model Federated Learning](<https://>) <font color="#f00">再上傳筆記</font>
10. **others**
    1. **resourece allocation for model aggregation in multitask FL**
        * [A Federated Learning Multi-Task Scheduling Mechanism Based on Trusted Computing Sandbox](<https://hackmd.io/@nnlijpg3Ts6jTgdB-pUfEw/HJJLVFKrT>)

# 還沒分類
1. [DRL-Based Joint Resource Allocation and Device Orchestration for Hierarchical Federated Learning in NOMA-Enabled Industrial IoT](<https://hackmd.io/9LoPBduiQGS0BOH-XXUeyg?view>)
    * RL,同時考慮到computation以及communication的resource allocation
2. [Exploring Deep-Reinforcement-Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Preserving EdgeIoT](<https://hackmd.io/DjGdL-goTM-beIzOe2gb9Q>) ~ 再整理清楚一些
    * 用RL TD3
    * 考慮到battery
    * action space超級大

other:
EMD怎麼算(非常清楚): https://www.hmoonotes.org/2020/06/earth-movers-distance.html



[XW+21][Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective](<https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9237168>)


