FGL studies
========================

Here we present a summary of papers in the FGL field.


Graph-FL
----------

+--------------+--------------+-----------+---------------------------+
| Title        | Venue        | Year      | Materials                 |
+==============+==============+===========+===========================+
| Federated    | NeurIPS      | 2021      | `[Paper                   |
| Graph        |              |           | ] <https://proceedings.ne |
| Cl           |              |           | urips.cc/paper/2021/hash/ |
| assification |              |           | 9c6947bd95ae487c81d4e19d3 |
| over Non-IID |              |           | ed8cd6f-Abstract.html>`__ |
| Graphs       |              |           | `[Code] <https://github   |
|              |              |           | .com/Oxfordblue7/GCFL>`__ |
+--------------+--------------+-----------+---------------------------+
| Federated    | AAAI         | 2023      | `[Paper] <https://        |
| Learning on  |              |           | ojs.aaai.org/index.php/AA |
| Non-IID      |              |           | AI/article/view/26187>`__ |
| Graphs via   |              |           | `[Code] <https://github.  |
| Structural   |              |           | com/yuetan031/fedstar>`__ |
| Knowledge    |              |           |                           |
| Sharing      |              |           |                           |
+--------------+--------------+-----------+---------------------------+



Subgraph-FL
-------------

+--------------+--------------+-----------+---------------------------+
| Title        | Venue        | Year      | Materials                 |
+==============+==============+===========+===========================+
| Subgraph     | NeurIPS      | 2021      | `[Paper                   |
| Federated    |              |           | ] <https://proceedings.ne |
| Learning     |              |           | urips.cc/paper/2021/hash/ |
| with Missing |              |           | 34adeb8e3242824038aa65460 |
| Neighbor     |              |           | a47c29e-Abstract.html>`__ |
| Generation   |              |           | `[Code] <https://git      |
|              |              |           | hub.com/zkhku/fedsage>`__ |
+--------------+--------------+-----------+---------------------------+
| FedGSL:      | ICBD         | 2022      | `[Paper]                  |
| Federated    |              |           | <https://ieeexplore.ieee. |
| Graph        |              |           | org/document/10020771>`__ |
| Structure    |              |           |                           |
| Learning for |              |           |                           |
| Local        |              |           |                           |
| Subgraph     |              |           |                           |
| Augmentation |              |           |                           |
+--------------+--------------+-----------+---------------------------+
| Federated    | WWW          | 2023      | `[Paper] <http            |
| Node         |              |           | s://dl.acm.org/doi/abs/10 |
| Cl           |              |           | .1145/3543507.3583471>`__ |
| assification |              |           | `[Code] <https://github.c |
| over Graphs  |              |           | om/Oxfordblue7/FedLIT>`__ |
| with Latent  |              |           |                           |
| Link-type    |              |           |                           |
| H            |              |           |                           |
| eterogeneity |              |           |                           |
+--------------+--------------+-----------+---------------------------+
| FedHGN: a    | IJCAI        | 2023      | `[Paper] <http            |
| federated    |              |           | s://dl.acm.org/doi/abs/10 |
| framework    |              |           | .24963/ijcai.2023/412>`__ |
| for          |              |           | `[Code] <https://githu    |
| h            |              |           | b.com/cynricfu/FedHGN>`__ |
| eterogeneous |              |           |                           |
| graph neural |              |           |                           |
| networks     |              |           |                           |
+--------------+--------------+-----------+---------------------------+
| Federated    | IJCAI        | 2023      | `[Paper] <h               |
| graph        |              |           | ttps://www.ijcai.org/proc |
| semantic and |              |           | eedings/2023/0426.pdf>`__ |
| structural   |              |           | `[Code] <https://github   |
| learning     |              |           | .com/WenkeHuang/FGSSL>`__ |
+--------------+--------------+-----------+---------------------------+
| Globally     | IJCAI        | 2023      | `[Paper] <h               |
| Consistent   |              |           | ttps://www.ijcai.org/proc |
| Federated    |              |           | eedings/2023/0419.pdf>`__ |
| Graph        |              |           | `[Code] <https://gith     |
| Autoencoder  |              |           | ub.com/gcfgae/GCFGAE/>`__ |
| for Non-IID  |              |           |                           |
| Graphs       |              |           |                           |
+--------------+--------------+-----------+---------------------------+
| AdaFGL: A    | ICDE         | 2024      | `[Paper] <https://arx     |
| New Paradigm |              |           | iv.org/abs/2401.11750>`__ |
| for          |              |           | `[Code] <https://github.  |
| Federated    |              |           | com/xkLi-Allen/AdaFGL>`__ |
| Node         |              |           |                           |
| Cl           |              |           |                           |
| assification |              |           |                           |
| with         |              |           |                           |
| Topology     |              |           |                           |
| H            |              |           |                           |
| eterogeneity |              |           |                           |
+--------------+--------------+-----------+---------------------------+
| FedGTA:      | VLDB         | 2024      | `[Paper] <https           |
| To           |              |           | ://dl.acm.org/doi/abs/10. |
| pology-aware |              |           | 14778/3617838.3617842>`__ |
| Averaging    |              |           | `[Code] <https://github.  |
| for          |              |           | com/xkLi-Allen/FedGTA>`__ |
| Federated    |              |           |                           |
| Graph        |              |           |                           |
| Learning     |              |           |                           |
+--------------+--------------+-----------+---------------------------+
| Federated    | AAAI         | 2024      | `[Paper] <https://        |
| Graph        |              |           | ojs.aaai.org/index.php/AA |
| Learning     |              |           | AI/article/view/29468>`__ |
| under Domain |              |           | `[Code] <https://github.  |
| Shift with   |              |           | com/GuanchengWan/FGGP>`__ |
| G            |              |           |                           |
| eneralizable |              |           |                           |
| Prototypes   |              |           |                           |
+--------------+--------------+-----------+---------------------------+
| FedGT:       | arXiv        | 2024      | `[Paper] <https://arx     |
| Federated    |              |           | iv.org/abs/2401.15203>`__ |
| Node         |              |           |                           |
| Cl           |              |           |                           |
| assification |              |           |                           |
| with         |              |           |                           |
| Scalable     |              |           |                           |
| Graph        |              |           |                           |
| Transformer  |              |           |                           |
+--------------+--------------+-----------+---------------------------+
| FedGL:       | IS           | 2024      | `[Pape                    |
| Federated    |              |           | r] <https://www.sciencedi |
| graph        |              |           | rect.com/science/article/ |
| learning     |              |           | pii/S002002552301561X>`__ |
| framework    |              |           |                           |
| with global  |              |           |                           |
| self         |              |           |                           |
| -supervision |              |           |                           |
+--------------+--------------+-----------+---------------------------+
| Deep         | SDM          | 2024      | `[Paper] <https://epu     |
| Efficient    |              |           | bs.siam.org/doi/abs/10.11 |
| Private      |              |           | 37/1.9781611978032.92>`__ |
| Neighbor     |              |           |                           |
| Generation   |              |           |                           |
| for Subgraph |              |           |                           |
| Federated    |              |           |                           |
| Learning     |              |           |                           |
+--------------+--------------+-----------+---------------------------+


Survey / Library / Benchmarks
-------------------------------

+--------------+--------------+-----------+---------------------------+
| Title        | Venue        | Year      | Materials                 |
+==============+==============+===========+===========================+
| Federated    | arXiv        | 2021      | `[Paper] <https://arx     |
| graph        |              |           | iv.org/abs/2105.11099>`__ |
| learning–a   |              |           |                           |
| position     |              |           |                           |
| paper        |              |           |                           |
+--------------+--------------+-----------+---------------------------+
| FedGraphNN:  | arXiv        | 2021      | `[Paper] <https://arx     |
| A Federated  |              |           | iv.org/abs/2104.07145>`__ |
| Learning     |              |           | `                         |
| System and   |              |           | [Code] <https://github.co |
| Benchmark    |              |           | m/FedML-AI/FedGraphNN>`__ |
| for Graph    |              |           |                           |
| Neural       |              |           |                           |
| Networks     |              |           |                           |
+--------------+--------------+-----------+---------------------------+
| Federated    | SIGKDD       | 2022      | `[Paper] <http            |
| graph        |              |           | s://dl.acm.org/doi/abs/10 |
| machine      |              |           | .1145/3575637.3575644>`__ |
| learning: A  |              |           |                           |
| survey of    |              |           |                           |
| concepts,    |              |           |                           |
| techniques,  |              |           |                           |
| and          |              |           |                           |
| applications |              |           |                           |
+--------------+--------------+-----------+---------------------------+
| Federat      | KDD          | 2022      | `[Paper] <http            |
| edscope-gnn: |              |           | s://dl.acm.org/doi/abs/10 |
| Towards a    |              |           | .1145/3534678.3539112>`__ |
| unified,     |              |           | `[Co                      |
| c            |              |           | de] <https://github.com/a |
| omprehensive |              |           | libaba/FederatedScope>`__ |
| and          |              |           |                           |
| efficient    |              |           |                           |
| package for  |              |           |                           |
| federated    |              |           |                           |
| graph        |              |           |                           |
| learning     |              |           |                           |
+--------------+--------------+-----------+---------------------------+
| Federated    | TNNLS        | 2024      | `[Paper] <https://        |
| Graph Neural |              |           | ieeexplore.ieee.org/abstr |
| Networks:    |              |           | act/document/10428063>`__ |
| Overview,    |              |           |                           |
| Techniques,  |              |           |                           |
| and          |              |           |                           |
| Challenges   |              |           |                           |
+--------------+--------------+-----------+---------------------------+