auto update @ 2023-02-19T14:21:56Z Asia/Shanghai

README.md

@@ -963,13 +963,13 @@ In this section, we will summarize Federated Learning papers accepted by top Dat
 | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------- | ---- | ----------------------------------------- | ------------------------------------------------------------ |
 | A Hierarchical Knowledge Transfer Framework for Heterogeneous Federated Learning | THU | INFOCOM | 2023 |  |  |
 | A Reinforcement Learning Approach for Minimizing Job Completion Time in Clustered Federated Learning | Southeast University | INFOCOM | 2023 |  |  |
-| Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning | USTC | INFOCOM | 2023 | FedHP[^FedHP] | [[pdf](https://arxiv.org/abs/2212.02136)] |
-| AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices | Guangdong University of Technology | INFOCOM | 2023 | AnycostFL[^AnycostFL] | [[pdf](https://arxiv.org/abs/2301.03062)] |
+| Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning | USTC | INFOCOM | 2023 | FedHP[^FedHP] | [[PDF](https://arxiv.org/abs/2212.02136)] |
+| AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices | Guangdong University of Technology | INFOCOM | 2023 | AnycostFL[^AnycostFL] | [[PDF](https://arxiv.org/abs/2301.03062)] |
 | AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation | HUST | INFOCOM | 2023 | AOCC-FL[^AOCC-FL] |  |
-| Asynchronous Federated Unlearning | University of Toronto | INFOCOM | 2023 | KNOT[^KNOT] | [[pdf](https://iqua.ece.toronto.edu/papers/ningxinsu-infocom23.pdf)] |
-| Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization | PSU | INFOCOM | 2023 |  | [[pdf](https://arxiv.org/abs/2212.08272)] |
+| Asynchronous Federated Unlearning | University of Toronto | INFOCOM | 2023 | KNOT[^KNOT] | [[PDF](https://iqua.ece.toronto.edu/papers/ningxinsu-infocom23.pdf)] |
+| Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization | PSU | INFOCOM | 2023 |  | [[PDF](https://arxiv.org/abs/2212.08272)] |
 | Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing | Beihang University | INFOCOM | 2023 |  |  |
-| Federated Learning under Heterogeneous and Correlated Client Availability | Inria | INFOCOM | 2023 | CA-Fed[^CA-Fed] | [[pdf](https://arxiv.org/abs/2301.04632)] [code](https://github.com/arodio/ca-fed) |
+| Federated Learning under Heterogeneous and Correlated Client Availability | Inria | INFOCOM | 2023 | CA-Fed[^CA-Fed] | [[PDF](https://arxiv.org/abs/2301.04632)] [[CODE](https://github.com/arodio/ca-fed)] |
 | Federated Learning with Flexible Control | IBM | INFOCOM | 2023 | FlexFL[^FlexFL] | [[PDF](https://arxiv.org/abs/2212.08496)] |
 | Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks | The University of Sydney | INFOCOM | 2023 |  | [[PDF](https://arxiv.org/abs/2212.12121)] |
 | FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection | HUST | INFOCOM | 2023 | FedMoS[^FedMoS] | [[PDF](https://wangxionghome.github.io/MainFL-TR.pdf)] |
@@ -985,7 +985,7 @@ In this section, we will summarize Federated Learning papers accepted by top Dat
 | SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition | Beihang University | INFOCOM | 2023 | SVDFed[^SVDFed] |  |
 | Tackling System Induced Bias in Federated Learning: Stratification and Convergence Analysis | Southern University of Science and Technology | INFOCOM | 2023 |  | [[PDF](https://arxiv.org/abs/2112.11256)] |
 | Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions | BUPT | INFOCOM | 2023 |  | [[PDF](http://ix.cs.uoregon.edu/~jiao/publications/infocom23-fl.pdf)] |
-| Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling | Auburn University | INFOCOM | 2023 |  | [[pdf](https://arxiv.org/abs/2302.00106)] |
+| Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling | Auburn University | INFOCOM | 2023 |  | [[PDF](https://arxiv.org/abs/2302.00106)] |
 | TVFL: Tunable Vertical Federated Learning towards Communication-Efficient Model Serving | USTC | INFOCOM | 2023 | TVFL[^TVFL] |  |
 | PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection for Efficient Federated Learning | MSU | MobiCom | 2022 | PyramidFL[^PyramidFL] | [[PUB](https://dl.acm.org/doi/10.1145/3495243.3517017)] [[PDF](https://www.egr.msu.edu/~mizhang/papers/2022_MobiCom_PyramidFL.pdf)] [[CODE](https://github.com/liecn/PyramidFL)] |
 | NestFL: efficient federated learning through progressive model pruning in heterogeneous edge computing | pmlabs | MobiCom(Poster) | 2022 |  | [[PUB](https://dl.acm.org/doi/10.1145/3495243.3558248)] |

data.yaml

@@ -8146,15 +8146,15 @@ fl-in-top-network-conference-and-journal:
     year: '2023'
     tldr: 'FedHP: TBC'
     materials:
-      PUB: https://arxiv.org/abs/2212.02136
+      PDF: https://arxiv.org/abs/2212.02136
   - title: 'AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge
       Devices'
     affiliation: Guangdong University of Technology
     venue: INFOCOM
     year: '2023'
     tldr: 'AnycostFL: TBC'
     materials:
-      PUB: https://arxiv.org/abs/2301.03062
+      PDF: https://arxiv.org/abs/2301.03062
   - title: 'AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation'
     affiliation: HUST
     venue: INFOCOM
@@ -8167,15 +8167,15 @@ fl-in-top-network-conference-and-journal:
     year: '2023'
     tldr: 'KNOT: TBC'
     materials:
-      PUB: https://iqua.ece.toronto.edu/papers/ningxinsu-infocom23.pdf
+      PDF: https://iqua.ece.toronto.edu/papers/ningxinsu-infocom23.pdf
   - title: Communication-Efficient Federated Learning for Heterogeneous Edge Devices
       Based on Adaptive Gradient Quantization
     affiliation: PSU
     venue: INFOCOM
     year: '2023'
     tldr: ''
     materials:
-      PUB: https://arxiv.org/abs/2212.08272
+      PDF: https://arxiv.org/abs/2212.08272
   - title: Enabling Communication-Efficient Federated Learning via Distributed Compressed
       Sensing
     affiliation: Beihang University
@@ -8189,29 +8189,30 @@ fl-in-top-network-conference-and-journal:
     year: '2023'
     tldr: 'CA-Fed: TBC'
     materials:
-      PUB: https://arxiv.org/abs/2301.04632
+      PDF: https://arxiv.org/abs/2301.04632
+      CODE: https://github.com/arodio/ca-fed
   - title: Federated Learning with Flexible Control
     affiliation: IBM
     venue: INFOCOM
     year: '2023'
     tldr: 'FlexFL: TBC'
     materials:
-      PUB: https://arxiv.org/abs/2212.08496
+      PDF: https://arxiv.org/abs/2212.08496
   - title: Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks
     affiliation: The University of Sydney
     venue: INFOCOM
     year: '2023'
     tldr: ''
     materials:
-      PUB: https://arxiv.org/abs/2212.12121
+      PDF: https://arxiv.org/abs/2212.12121
   - title: 'FedMoS: Taming Client Drift in Federated Learning with Double Momentum
       and Adaptive Selection'
     affiliation: HUST
     venue: INFOCOM
     year: '2023'
     tldr: 'FedMoS: TBC'
     materials:
-      PUB: https://wangxionghome.github.io/MainFL-TR.pdf
+      PDF: https://wangxionghome.github.io/MainFL-TR.pdf
   - title: 'FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated
       Learning'
     affiliation: NTU
@@ -8247,14 +8248,14 @@ fl-in-top-network-conference-and-journal:
     year: '2023'
     tldr: 'OUTPOST: TBC'
     materials:
-      PUB: https://iqua.ece.toronto.edu/papers/feiwang-infocom23.pdf
+      PDF: https://iqua.ece.toronto.edu/papers/feiwang-infocom23.pdf
   - title: 'Network Adaptive Federated Learning: Congestion and Lossy Compression'
     affiliation: UTAustin
     venue: INFOCOM
     year: '2023'
     tldr: 'NAC-FL: TBC'
     materials:
-      PUB: https://arxiv.org/abs/2301.04430
+      PDF: https://arxiv.org/abs/2301.04430
   - title: 'OBLIVION: Poisoning Federated Learning by Inducing Catastrophic Forgetting'
     affiliation: The Hang Seng University of Hong Kong
     venue: INFOCOM
@@ -8274,7 +8275,7 @@ fl-in-top-network-conference-and-journal:
     year: '2023'
     tldr: 'SplitGP: TBC'
     materials:
-      PUB: https://arxiv.org/abs/2212.08343
+      PDF: https://arxiv.org/abs/2212.08343
   - title: 'SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition'
     affiliation: Beihang University
     venue: INFOCOM
@@ -8288,23 +8289,23 @@ fl-in-top-network-conference-and-journal:
     year: '2023'
     tldr: ''
     materials:
-      PUB: https://arxiv.org/abs/2112.11256
+      PDF: https://arxiv.org/abs/2112.11256
   - title: 'Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge
       Systems via Auctions'
     affiliation: BUPT
     venue: INFOCOM
     year: '2023'
     tldr: ''
     materials:
-      PUB: http://ix.cs.uoregon.edu/~jiao/publications/infocom23-fl.pdf
+      PDF: http://ix.cs.uoregon.edu/~jiao/publications/infocom23-fl.pdf
   - title: Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data
       Labeling
     affiliation: Auburn University
     venue: INFOCOM
     year: '2023'
     tldr: ''
     materials:
-      PUB: https://arxiv.org/abs/2302.00106
+      PDF: https://arxiv.org/abs/2302.00106
   - title: 'TVFL: Tunable Vertical Federated Learning towards Communication-Efficient
       Model Serving'
     affiliation: USTC