Meta-learning and Personalization layer in Federated learning

Official code for ACIIDS2022 paper "Meta-learning and Personalization layer in Federated learning".

General information

• Supervisor: Prof. Lê Hoài Bắc
• Reviewer: Dr. Nguyễn Tiến Huy
• Authors:
  • Nguyễn Bảo Long - MSSV: 18120201
  • Cao Tất Cường - MSSV: 18120296
• Reporting date: 15/03/2022 at Computer Science No.1, University of Science, VietNam National University - Ho Chi Minh City.

Contact

• Corresponding author: Bao-Long Nguyen
• Email: baolongnguyen.mac@gmail.com

How to run

• Dataset configuration: Dataset is configured as in Personalized Federated Learning with Moreau Envelopes (NeurIPS 2020).

• 2 ways to run simulation (read Flower's doc for more detail):

  • Normal mode: Run file run.sh. This file contains all the command codes that output the results of this thesis. After running this file, function start_simulation() in file ./main.py will be called.

  • Debug mode: Run 2 files ./run_client.sh and ./run_server.sh. File ./run_server.sh calls main() in file ./server/server_main.py in order to create and run a server. File ./run_client.sh creates a certain number of clients by calling function main() in file ./client/client_main.py multiple times.

Some information about source code

• Folder ./client: Defines types of clients of FL systems (based on Flower framework).

• Folder ./client_worker: Defines training methods (meta learning, using learn2learn and conventional FL training) and testing methods. These functions in here will be called by clients in ./client.

• Folder ./data: Contains data generator (./data/mnist, ./data/cifar), and data loader (./data/dataloaders) for each client.

• Folder ./document: Contains a presentation, a thesis and relevant documents.

• Folder ./experiments: Results of FedAvg, FedAvgMeta, FedPer, FedPerMeta, FedMeta(MAML), FedMeta(Meta-SGD), FedMeta-Per(MAML), FedMeta-Per(Meta-SGD) running on MNIST, CIFAR-10, and on 2 types of client (new client, local client).

• Folder ./model: Defines models and model wrapper for MNIST, CIFAR-10.

• Folder ./personalized_weight: A temporary folder, generated during the execution of algorithms using personalization layer. This folder contains personalization layer of each client.

Results

• We proposed FedMeta-Per (FedMeta-Per(MAML), FedMeta-Per(Meta-SGD)), a combination of Meta-learning and Personalization layer into a FL system.

MNIST

• Classification results (%) of local client using MNIST dataset

	$acc_{micro}$	$acc_{macro}$	$P_{macro}$	$R_{macro}$	$F1_{macro}$
FedAvg	85.03	82.14±14.76	82.03±13.88	81.54±14.33	79.43±16.83
FedPer	77.29	75.48±14.84	76.07±14.99	74.01±15.13	72.32±15.99
FedAvgMeta	84.84	81.56±16.68	80.71±17.02	81.18±16.16	78.31±19.8
FedPerMeta	75.91	74.11±16.2	75.68±15.94	72.93±15.58	71.22±16.77
FedMeta(MAML)	92.99	91.14±5.99	90.56±6.24	90.98±5.9	90.16±6.28
FedMeta(Meta-SGD)	98.02	96.35±4.62	96.49±4.1	95.64±5.94	95.80±5.51
FedMeta-Per(MAML)	99.37	99.12±1.29	99.11±1.3	98.82±1.99	98.94±1.6
FedMeta-Per(Meta-SGD)	98.92	98.15±3.32	98.42±1.95	98.42±1.96	98.20±2.94

• Classification results (%) on new client using MNIST dataset

	$acc_{micro}$	$acc_{macro}$	$P_{macro}$	$R_{macro}$	$F1_{macro}$
FedAvg	83.92	81.69±19.71	79.57±20.18	80.46±17.84	77.66±22.54
FedPer	78.3	76.19±18.79	75.91±17.52	74.73±17.32	72.72±19.3
FedAvgMeta	84.34	82.37±17.42	81.38±16.25	80.91±15.62	78.78±19.31
FedPerMeta	77.47	75.56±20.33	75.09±19.52	74.92±18.85	72.60±21.37
FedMeta(MAML)	92.96	91.88±5.88	90.14±7.97	90.74±5.95	90.02±7.34
FedMeta(Meta-SGD)	96.39	93.53±8.39	93.73±10.26	88.65±14.06	89.31±14.56
FedMeta-Per(MAML)	93.6	93.57±5.58	93.64±5.56	90.98±6.98	91.83±6.43
FedMeta-Per(Meta-SGD)	96.62	95.88±3.58	95.73±4.11	94.34±5.05	94.85±4.61

CIFAR-10

• Classification results (%) of local client using CIFAR-10 dataset

	$acc_{micro}$	$acc_{macro}$	$P_{macro}$	$R_{macro}$	$F1_{macro}$
FedAvg	19.02	19.29±25.11	15.57±23.7	20.65±25.55	16.85±23.92
FedPer	13.22	12.99±19.39	18.34±28.59	14.14±20.83	10.52±14.91
FedAvgMeta	40.3	38.47±31.52	32.84±32.06	39.33±30.35	33.81±30.61
FedPerMeta	18.57	17.48±22.55	20.02±27.4	18.43±23.47	14.54±18.67
FedMeta(MAML)	69.02	68.76±14.86	67.42±21.16	66.56±13.48	61.14±20
FedMeta(Meta-SGD)	78.63	78.73±11.59	74.65±21.12	75.25±14.09	72.87±18.31
FedMeta-Per(MAML)	86.6	86.52±6.31	86.43±5.88	85.47±6.87	85.33±6.77
FedMeta-Per(Meta-SGD)	85.61	85.68±7.22	86.26±6.35	85.36±6.83	85.08±7.32

• Classification results (%) of new client using CIFAR-10 dataset

	$acc_{micro}$	$acc_{macro}$	$P_{macro}$	$R_{macro}$	$F1_{macro}$
FedAvg	24.63	24.83±22.57	18.36±20.15	24.44±21.95	20.52±20.45
FedPer	14.4	14.52±20.15	12.59±20.65	14.23±19.58	10.66±13.79
FedAvgMeta	43.39	43.54±18	33.45±21.44	42.87±16.98	35.14±17.22
FedPerMeta	13.33	13.57±19.62	11.99±19.52	13.53±19.08	10.05±13.17
FedMeta(MAML)	61.69	61.64±12.49	52.66±26.06	59.94±12.35	50.76±19.2
FedMeta(Meta-SGD)	68.36	67.89±15.11	70.3±22.37	66.86±15.02	60.24±21.52
FedMeta-Per(MAML)	64.22	63.70±12.29	57.06±24.99	61.63±12.66	53.68±19.06
FedMeta-Per(Meta-SGD)	69.97	69.13±14.63	66.53±24.91	67.82±15.34	62.42±20.94

Visualization

• FedMeta-Per vs. (FedAvg, FedAvgMeta, FedPer, FedPerMeta): The proposed methods achieves higher degree in term of convergence and accuracy compared with FedAvg and FedPer.

• FedMeta-Per vs. FedMeta: Improved personalization is the reason why results on local clients of FedMeta-Per achieve faster convergence and higher accuracy than FedMeta. Regarding the new clients, 2 algorithms achieve the same degree of convergence. However, the personalization layer at each FedMeta-Per client will improve over time as the client participates in one or more local training step (new client becomes local client).