Examples

In examples/, we included a wide variety of examples that showcased how third-party deep learning frameworks, such as Catalyst, can be used, and how a collection of federated learning algorithms in the research literature can be implemented using Plato by customizing the client, server, algorithm, and trainer. We also included detailed tutorials on how Plato can be run on Google Colab. Here is a list of the examples we included.

Support for Third-Party Frameworks

Catalyst

Plato supports the use of third-party frameworks for its training loops. This example shows how Catalyst can be used with Plato for local training and testing on the clients. This example uses a very simple PyTorch model and the MNIST dataset to show how the model, the training and validation datasets, as well as the training and testing loops can be quickly customized in Plato.

python examples/third_party/catalyst/catalyst_example.py -c examples/third_party/catalyst/catalyst_fedavg_lenet5.yml

Server Aggregation Algorithms

FedAtt

FedAtt is a server aggregation algorithm, where client updates were aggregated using a layer-wise attention-based mechanism that considered the similarity between the server and client models. The objective was to improve the accuracy or perplexity of the trained model with the same number of communication rounds. In its implementation in examples/fedatt/fedatt_algorithm.py, the PyTorch implementation of FedAtt overrides aggregate_weights() to implement FedAtt as a custom server aggregation algorithm.

python examples/server_aggregation/fedatt/fedatt.py -c examples/server_aggregation/fedatt/fedatt_FashionMNIST_lenet5.yml

Note

S. Ji, S. Pan, G. Long, X. Li, J. Jiang, Z. Huang. “Learning Private Neural Language Modeling with Attentive Aggregation,” in Proc. International Joint Conference on Neural Networks (IJCNN), 2019.

FedAdp

FedAdp is another server aggregation algorithm, which exploited the implicit connection between data distribution on a client and the contribution from that client to the global model, measured at the server by inferring gradient information of participating clients. In its implementation in examples/fedadp/fedadp_server.py, a framework-agnostic implementation of FedAdp overrides aggregate_deltas() to implement FedAdp as a custom server aggregation algorithm.

python examples/server_aggregation/fedadp/fedadp.py -c examples/server_aggregation/fedadp/fedadp_FashionMNIST_lenet5.yml

Note

H. Wu, P. Wang. “Fast-Convergent Federated Learning with Adaptive Weighting,” in IEEE Trans. on Cognitive Communications and Networking (TCCN), 2021.

Secure Aggregation with Homomorphic Encryption

MaskCrypt

MaskCrypt is a secure federated learning system based on homomorphic encryption. Instead of encrypting all the model updates, MaskCrypt encrypts only part of them to balance the tradeoff between security and efficiency. In this example, clients only select 5% of the model updates to encrypt during the learning process. The number of encrypted weights is determined by encrypt_ratio, which can be adjusted in the configuration file. A random mask will be adopted if random_mask is set to true.

python examples/secure_aggregation/maskcrypt/maskcrypt.py -c examples/secure_aggregation/maskcrypt/maskcrypt_MNIST_lenet5.yml

Asynchronous Federated Learning Algorithms

FedAsync

FedAsync is one of the first algorithms proposed in the literature towards operating federated learning training sessions in asynchronous mode, which Plato supports natively. It advocated aggregating aggressively whenever only one client reported its local updates to the server.

In its implementation, FedAsync’s server subclasses from the FedAvg server and overrides its configure() and aggregate_weights() functions. In configure(), it needs to add some custom features (of obtaining a mixing hyperparameter for later use in the aggregation process), and calls super().configure() first, similar to its __init__() function calling super().__init__(). When it overrides aggregate_weights(), however, it supplied a completely custom implementation of this function.

python examples/async/fedasync/fedasync.py -c examples/async/fedasync/fedasync_MNIST_lenet5.yml
python examples/async/fedasync/fedasync.py -c examples/async/fedasync/fedasync_CIFAR10_resnet18.yml

Note

C. Xie, S. Koyejo, I. Gupta. “Asynchronous Federated Optimization,” in Proc. Annual Workshop on Optimization for Machine Learning (OPT), 2020.

Port

Port is one of the federated learning training sessions in asynchronous mode. The server will aggregate when it receives a minimum number of clients’ updates, which can be tuned with ‘minimum_clients_aggregated’. The ‘staleness_bound’ is also a common parameter in asynchronous FL, which limit the staleness of all clients’ updates. ‘request_update’ is a special design in Port, to force clients report their updates and shut down the training process if their too slow. ‘similarity_weight’ and ‘staleness_weight’ are two hyper-parameters in Port, tuning the weights of them when the server do aggregation. ‘max_sleep_time’, ‘sleep_simulation’, ‘avg_training_time’ and ‘simulation_distribution’ are also important to define the arrival clients in Port.

python examples/async/port/port.py -c examples/async/port/port_cifar10.yml

Note

N. Su, B. Li. “How Asynchronous can Federated Learning Be?,” in Proc. IEEE/ACM International Symposium on Quality of Service (IWQoS), 2022.

Federated Unlearning

Federated Unlearning

Federated unlearning is a concept proposed in the recent research literature that uses an unlearning algorithm, such as retraining from scratch, to guarantee that a client is able to remove all the effects of its local private data samples from the trained model. In its implementation in examples/unlearning/fedunlearning/fedunlearning_server.py and examples/unlearning/fedunlearning/fedunlearning_client.py, a framework-agnostic implementation of federated unlearning overrides several methods in the client and server APIs, such as the server’s aggregate_deltas() to implement federated unlearning.

python examples/unlearning/fedunlearning/fedunlearning.py -c examples/unlearning/fedunlearning/fedunlearning_adahessian_MNIST_lenet5.yml

Note

If the AdaHessian optimizer is used as in the example configuration file, it will reflect what the following paper proposed:

Liu et al., “The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining,” in Proc. INFOCOM, 2022.

Knot

Knot is implemented in examples/unlearning, which clusters the clients, and the server aggregation is carried out within each cluster only. Knot is designed under asynchronous mode, and unlearned by retraining from scratch in cluster. The global model will be aggregated at the end of the retraining process. Knot supports a wide range of tasks, including image classification and language tasks.

python examples/unlearning/knot/knot.py -c examples/unlearning/knot/knot_cifar10_resnet18.yml
python examples/unlearning/knot/knot.py -c examples/unlearning/knot/knot_mnist_lenet5.yml
python examples/unlearning/knot/knot.py -c examples/unlearning/knot/knot_purchase.yml

Note

N. Su, B. Li. “Asynchronous Federated Unlearning,” in Proc. IEEE International Conference on Computer Communications (INFOCOM 2023).

Gradient Leakage Attacks and Defences

Gradient leakage attacks and defenses

Gradient leakage attacks and their defenses have been extensively studied in the research literature on federated learning. In examples/gradient_leakage_attacks/, several attacks, including DLG, iDLG, and csDLG, have been implemented, as well as several defense mechanisms, including Soteria, GradDefense, Differential Privacy, Gradient Compression, and Outpost. A variety of methods in the trainer API has been used in their implementations. Refer to examples/dlg/README.md for more details.

python examples/gradient_leakage_attacks/dlg.py -c examples/gradient_leakage_attacks/reconstruction_emnist.yml --cpu

Algorithms with Customized Client Training Loops

SCAFFOLD

SCAFFOLD is a synchronous federated learning algorithm that performs server aggregation with control variates to better handle statistical heterogeneity. It has been quite widely cited and compared with in the federated learning literature. In this example, two processors, called ExtractControlVariatesProcessor and SendControlVariateProcessor, have been introduced to the client using a callback class, called ScaffoldCallback. They are used for sending control variates between the clients and the server. Each client also tries to maintain its own control variates for local optimization using files.

python examples/customized_client_training/scaffold/scaffold.py -c examples/customized_client_training/scaffold/scaffold_MNIST_lenet5.yml

Note

Karimireddy et al., “SCAFFOLD: Stochastic Controlled Averaging for Federated Learning, ” in Proc. International Conference on Machine Learning (ICML), 2020.

FedProx

To better handle system heterogeneity, the FedProx algorithm introduced a proximal term in the optimizer used by local training on the clients. It has been quite widely cited and compared with in the federated learning literature.

python examples/customized_client_training/fedprox/fedprox.py -c examples/customized_client_training/fedprox/fedprox_MNIST_lenet5.yml

Note

T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, V. Smith. “Federated Optimization in Heterogeneous Networks,” in Proc. Machine Learning and Systems (MLSys), 2020.

FedDyn

FedDyn is proposed to provide communication savings by dynamically updating each participating device’s regularizer in each round of training. It is a method proposed to solve data heterogeneity in federated learning.

python examples/customized_client_training/feddyn/feddyn.py -c examples/customized_client_training/feddyn/feddyn_MNIST_lenet5.yml

Note

Acar, D.A.E., Zhao, Y., Navarro, R.M., Mattina, M., Whatmough, P.N. and Saligrama, V. “Federated learning based on dynamic regularization,” Proceedings of International Conference on Learning Representations (ICLR), 2021.

FedTI

FedTI is to perform textual inversion under federated learning, which we can simply refer to as federated textual inversion. This approach treats the learnable pseudo-word embedding as the global model, and thus allows clients to train cooperatively using the FL paradigm.

python examples/customized_client_training/fedti/fedti.py -c examples/customized_client_training/fedti/StableDiffusionFed_iid_50.yml -b FedTI

Note

Gal et al., “POLARIS: An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion, ” Arxiv, 2022.

FedMos

FedMoS is a communication-efficient FL framework with coupled double momentum-based update and adaptive client selection, to jointly mitigate the intrinsic variance.

python examples/customized_client_training/fedmos/fedmos.py -c examples/customized_client_training/fedmos/fedmos_MNIST_lenet5.yml

Note

X. Wang, Y. Chen, Y. Li, X. Liao, H. Jin and B. Li, “FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection,” IEEE INFOCOM 2023.

Client Selection Algorithms

Active Federated Learning

Active Federated Learning is a client selection algorithm, where clients were selected not uniformly at random in each round, but with a probability conditioned on the current model and the data on the client to maximize training efficiency. The objective was to reduce the number of required training iterations while maintaining the same model accuracy. In its implementation in examples/afl/afl_server.py, the server overrides choose_clients() to implement a custom client selection algorithm, and overrides weights_aggregated() to extract additional information from client reports.

python examples/client_selection/afl/afl.py -c examples/client_selection/afl/afl_FashionMNIST_lenet5.yml

Note

J. Goetz, K. Malik, D. Bui, S. Moon, H. Liu, A. Kumar. “Active Federated Learning,” September 2019.

Pisces

Pisces is an asynchronous federated learning algorithm that performs biased client selection based on overall utilities and weighted server aggregation based on staleness. In this example, a client running the Pisces algorithm calculates its statistical utility and report it together with model updates to Pisces server. The server then evaluates the overall utility for each client based on the reported statistical utility and client staleness, and selects clients for the next communication round. The algorithm also attempts to detect outliers via DBSCAN for better robustness.

python examples/client_selection/pisces/pisces.py -c examples/client_selection/pisces/pisces_MNIST_lenet5.yml

Note

Jiang et al., “Pisces: Efficient Federated Learning via Guided Asynchronous Training, ” in Proc. ACM Symposium on Cloud Computing (SoCC), 2022.

Oort

Oort is a federated learning algorithm that performs biased client selection based on both statistical utility and system utility. Originally, Oort is proposed for synchronous federated learning. In this example, it was adapted to support both synchronous and asynchronous federated learning. Notably, the Oort server maintains a blacklist for clients that have been selected too many times (10 by default). If per_round / total_clients is large, e.g. 2/5, the Oort server may not work correctly because most clients are in the blacklist and there will not be a sufficient number of clients that can be selected.

python examples/client_selection/oort/oort.py -c examples/client_selection/oort/oort_MNIST_lenet5.yml

Note

Lai et al., “Oort: Efficient Federated Learning via Guided Participant Selection,” in Proc. USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2021.

Polaris

Polaris is a client selection method for asynchronous federated learning. In this method, it selects clients via balancing between local device speed and local data quality from an optimization perspective. As it does not require extra information rather than local updates, Polaris is pluggable to any other federated aggregation methods.

python3 examples/client_selection/polaris/polaris.py -c examples/client_selection/polaris/polaris_LeNet5.yml

Note

Kang et al., “[POLARIS: Accelerating Asynchronous Federated Learning with Client Selection], ”

Split Learning Algorithms

Split Learning

Split learning aims to collaboratively train deep learning models with the server performing a portion of the training process. In split learning, each training iteration is separated into two phases: the clients first send extracted features at a specific cut layer to the server, and then the server continues the forward pass and computes gradients, which will be sent back to the clients to complete the backward pass of the training. Unlike federated learning, split learning clients sequentially interact with the server, and the global model is synchronized implicitly through the model on the server side, which is shared and updated by all clients.

python ./run -c configs/CIFAR10/split_learning_resnet18.yml

Note

Vepakomma et al., “Split Learning for Health: Distributed Deep Learning without Sharing Raw Patient Data,” in Proc. NeurIPS, 2018.

Split Learning for Training ControlNet

ControlNet is a conditional image generation model that only finetunes the control network without updating parameters in the large diffusion model. It has a more complicated structure than the usual deep learning model. Hence, to train a ControlNet with split learning, the control network and a part of the diffusion model are on the clients and the remaining part of the diffusion model is on the server. The forwarding and backwarding processes are specifically designed according to the inputs and training targets of the image generation based on diffusion models.

python examples/split_learning/controlnet_split_learning/split_learning.py -c examples/split_learning/controlnet_split_learning/split_learning_fill50k.yml

Split Learning for Training LLM

This is an example of fine-tuning the Hugging Face large language model with split learning. The fine-tuning policy includes training the whole model and fine-tuning with the LoRA algorithm. The cut layer in the configuration file should be set as an integer, indicating cutting at which transformer block in the transformer model.

Fine-tune the whole model

python ./examples/split_learning/llm_split_learning/split_learning_main.py -c ./examples/split_learning/llm_split_learning/split_learning_wikitext103_gpt2.yml

Fine-tune with LoRA

python ./examples/split_learning/llm_split_learning/split_learning_main.py -c ./examples/split_learning/llm_split_learning/split_learning_wikitext2_gpt2_lora.yml

Personalized Federated Learning Algorithms

FedRep

FedRep learns a shared data representation (the global layers) across clients and a unique, personalized local ``head’’ (the local layers) for each client. In this implementation, after each round of local training, only the representation on each client is retrieved and uploaded to the server for aggregation.

python examples/personalized_fl/fedrep/fedrep.py -c examples/personalized_fl/configs/fedrep_CIFAR10_resnet18.yml

Note

Collins et al., “Exploiting Shared Representations for Personalized Federated Learning, ” in Proc. International Conference on Machine Learning (ICML), 2021.

FedBABU

FedBABU only updates the global layers of the model during FL training. The local layers are frozen at the beginning of each local training epoch.

python examples/personalized_fl/fedbabu/fedbabu.py -c examples/personalized_fl/configs/fedbabu_CIFAR10_resnet18.yml

Note

Oh et al., “FedBABU: Towards Enhanced Representation for Federated Image Classification, ” in Proc. International Conference on Learning Representations (ICLR), 2022.

APFL

APFL jointly optimizes the global model and personalized models by interpolating between local and personalized models. Once the global model is received, each client will carry out a regular local update, and then conduct a personalized optimization to acquire a trained personalized model. The trained global model and the personalized model will subsequently be combined using the parameter “alpha,” which can be dynamically updated.

python examples/personalized_fl/apfl/apfl.py -c examples/personalized_fl/configs/apfl_CIFAR10_resnet18.yml

Note

Deng et al., “Adaptive Personalized Federated Learning, ” in Arxiv, 2021.

FedPer

FedPer learns a global representation and personalized heads, but makes simultaneous local updates for both sets of parameters, therefore makes the same number of local updates for the head and the representation on each local round.

python examples/personalized_fl/fedper/fedper.py -c examples/personalized_fl/configs/fedper_CIFAR10_resnet18.yml

Note

Arivazhagan et al., “Federated learning with personalization layers, ” in Arxiv, 2019.

LG-FedAvg

With LG-FedAvg only the global layers of a model are sent to the server for aggregation, while each client keeps local layers to itself.

python examples/personalized_fl/lgfedavg/lgfedavg.py -c examples/personalized_fl/configs/lgfedavg_CIFAR10_resnet18.yml

Note

Liang et al., “Think Locally, Act Globally: Federated Learning with Local and Global Representations, ” in Proc. NeurIPS, 2019.

Ditto

Ditto jointly optimizes the global model and personalized models by learning local models that are encouraged to be close together by global regularization. In this example, once the global model is received, each client will carry out a regular local update and then optimizes the personalized model.

python examples/personalized_fl/ditto/ditto.py -c examples/personalized_fl/configs/ditto_CIFAR10_resnet18.yml

Note

Li et al., “Ditto: Fair and robust federated learning through personalization, ” in Proc ICML, 2021.

Per-FedAvg

Per-FedAvg uses the Model-Agnostic Meta-Learning (MAML) framework to perform local training during the regular training rounds. It performs two forward and backward passes with fixed learning rates in each iteration.

python examples/personalized_fl/perfedavg/perfedavg.py -c examples/personalized_fl/configs/perfedavg_CIFAR10_resnet18.yml

Note

Fallah et al., “Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach, ” in Proc NeurIPS, 2020.

Hermes

Hermes utilizes structured pruning to improve both communication efficiency and inference efficiency of federated learning. It prunes channels with the lowest magnitudes in each local model and adjusts the pruning amount based on each local model’s test accuracy and its previous pruning amount. When the server aggregates pruned updates, it only averages parameters that were not pruned on all clients.

python examples/personalized_fl/hermes/hermes.py -c examples/personalized_fl/configs/hermes_CIFAR10_resnet18.yml

Note

Li et al., “Hermes: An Efficient Federated Learning Framework for Heterogeneous Mobile Clients, ” in Proc. 27th Annual International Conference on Mobile Computing and Networking (MobiCom), 2021.

Personalized Federated Learning Algorithms based on Self-Supervised Learning

Self Supervised Learning

This category aims to achieve personalized federated learning by introducing self-supervised learning (SSL) to the training process. With SSL, an encoder model is trained to learn representations from unlabeled data. A higher performance can be achieved in subsequent tasks with the trained encoder. Only the encoder model is globally aggregated and shared during the regular training process. After reaching convergence, each client can download the trained global model to extract features from local samples. In this category, the following algorithms have been implemented:

  • SimCLR [1]

  • BYOL [2]

  • SimSiam [3]

  • MoCoV2 [4]

  • SwAV [5]

  • SMoG [6]

  • FedEMA [7]

  • Calibre

Note

Calibre is currently only supported on NVIDIA or M1/M2/M3 GPUs. To run on M1/M2/M3 GPUs, add the command-line argument -m.

python examples/ssl/simclr/simclr.py -c examples/ssl/configs/simclr_MNIST_lenet5.yml
python examples/ssl/simclr/simclr.py -c examples/ssl/configs/simclr_CIFAR10_resnet18.yml
python examples/ssl/byol/byol.py -c examples/ssl/configs/byol_CIFAR10_resnet18.yml
python examples/ssl/simsiam/simsiam.py -c examples/ssl/configs/simsiam_CIFAR10_resnet18.yml
python examples/ssl/moco/mocov2.py -c examples/ssl/configs/mocov2_CIFAR10_resnet18.yml
python examples/ssl/swav/swav.py -c examples/ssl/configs/swav_CIFAR10_resnet18.yml
python examples/ssl/smog/smog.py -c examples/ssl/configs/smog_CIFAR10_resnet18.yml
python examples/ssl/fedema/fedema.py -c examples/ssl/configs/fedema_CIFAR10_resnet18.yml
python examples/ssl/calibre/calibre.py -c examples/ssl/configs/calibre_CIFAR10_resnet18.yml

Note

[1] Chen et al., “A Simple Framework for Contrastive Learning of Visual Representations,” in Proc. ICML, 2020.

[2] Grill et al., “Bootstrap Your Own Latent A New Approach to Self-Supervised Learning, ” in Proc. NeurIPS, 2020.

[3] Chen et al., “Exploring Simple Siamese Representation Learning, ” in Proc. CVPR, 2021.

[4] Chen et al., “Improved Baselines with Momentum Contrastive Learning, ” in ArXiv, 2020.

[5] Caron et al., “Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, ” in Proc. NeurIPS, 2022.

[6] Pang et al., “Unsupervised Visual Representation Learning by Synchronous Momentum Grouping, ” in Proc. ECCV, 2022.

[7] Zhuang et al., “Divergence-Aware Federated Self-Supervised Learning, ” in Proc. ICLR, 2022.

Three-layer Federated Learning Algorithms

Tempo

Tempo is proposed to improve training performance in three-layer federated learning. It adaptively tunes the number of each client’s local training epochs based on the difference between its edge server’s locally aggregated model and the current global model.

python examples/three_layer_fl/tempo/tempo.py -c examples/three_layer_fl/tempo/tempo_MNIST_lenet5.yml

Note

Ying et al., “Tempo: Improving Training Performance in Cross-Silo Federated Learning, ” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022.

FedSaw

FedSaw is proposed to improve training performance in three-layer federated learning with L1-norm structured pruning. Edge servers and clients pruned their updates before sending them out. FedSaw adaptively tunes the pruning amount of each edge server and its clients based on the difference between the edge server’s locally aggregated model and the current global model.

python examples/three_layer_fl/fedsaw/fedsaw.py -c examples/three_layer_fl/fedsaw/fedsaw_MNIST_lenet5.yml

Poisoning Detection Algorithms

AsyncFilter

AsyncFilter is proposed to defend against untargeted poisoning attacks in asynchronous federated learning with a server filter. With statistical analysis, AsyncFilter identifies potential poisoned model updates and filters them out before the server aggregation stage.

python examples/detector/detector.py -c examples/detector/asyncfilter_fashion_6.yml

Note

Kang et al., “AsyncFilter: Detecting Poisoning Attacks in Asynchronous Federated Learning &rdquo: in the Proceedings of the 25th ACM/IFIP International Middleware Conference (Middleware), 2024.

Model Pruning Algorithms

FedSCR

FedSCR uses structured pruning to prune each update’s entire filters and channels if their summed parameter values are below a particular threshold.

python examples/model_pruning/fedscr/fedscr.py -c examples/model_pruning/fedscr/fedscr_MNIST_lenet5.yml

Note

Wu et al., “FedSCR: Structure-Based Communication Reduction for Federated Learning, ” IEEE Trans. Parallel Distributed Syst., 2021.

Sub-FedAvg

Sub-FedAvg aims to obtain a personalized model for each client with non-i.i.d. local data. It iteratively prunes the parameters of each client’s local model during its local training, with the objective of removing the commonly shared parameters of local models and keeping the personalized ones. Besides the original version for two-layer federated learning, the version for three-layer federated learning has been implemented as well.

For two-layer federated learning:

python examples/model_pruning/sub_fedavg/subfedavg.py -c examples/model_pruning/sub_fedavg/subfedavg_MNIST_lenet5.yml

For three-layer federated learning:

python examples/model_pruning/sub_fedavg/subcs.py -c examples/model_pruning/sub_fedavg/subcs_MNIST_lenet5.yml

Note

Vahidian et al., “Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity, ” in Proc. 41st IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW), 2021.

With the redesign of the Plato API, the following list is outdated and will be updated as they are tested again.

Method

Notes

Tested

Adaptive Freezing

Change directory to examples/adaptive_freezing and run python adaptive_freezing.py -c <configuration file>.

Yes

Gradient-Instructed Frequency Tuning

Change directory to examples/adaptive_sync and run python adaptive_sync.py -c <configuration file>.

Yes

Attack Adaptive

Change directory to examples/attack_adaptive and run python attack_adaptive.py -c <configuration file>.

Yes

Customizing clients and servers

This example shows how a custom client, server, and model can be built by using class inheritance in Python. Change directory to examples/customized and run python custom_server.py to run a standalone server (with no client processes launched), then run python custom_client.py to start a client that connects to a server running on localhost. To showcase how a custom model can be used, run python custom_model.py.

Yes

Running Plato in Google Colab

This example shows how Google Colab can be used to run Plato in a terminal. Two Colab notebooks have been provided as examples, one for running Plato directly in a Colab notebook, and another for running Plato in a terminal (which is much more convenient).

Yes

MistNet with separate client and server implementations

Change directory to examples/dist_mistnet and run python custom_server.py -c ./mistnet_lenet5_server.yml, then run python custom_client.py -c ./mistnet_lenet5_client.yml -i 1.

Yes

FedNova

Change directory to examples/fednova and run python fednova.py -c <configuration file>.

Yes

FedSarah

Change directory to examples/fedsarah and run python fedsarah.py -c <configuration file>.

Yes