The conventional machine learning (ML) and deep learning
(DL) methods use large amount of data to construct desirable prediction models in a central fusion center for recognizing human activities.
However, such model training encounters high communication costs and
leads to privacy infringement. To address the issues of high communication overhead and privacy leakage, we employed a widely popular distributed ML technique called Federated Learning (FL) that generates a
global model for predicting human activities by combining participated
agents’ local knowledge. The state-of-the-art FL model fails to maintain
acceptable accuracy when there is a large number of unreliable agents
who can infuse false model, or, resource-constrained agents that fails to
perform an assigned computational task within a given time window.
We developed an FL model for predicting human activities by monitoring agent’s contributions towards model convergence and avoiding the
unreliable and resource-constrained agents from training. We assign a
score to each client when it joins in a network and the score is updated
based on the agent’s activities during training. We consider three mobile
robots as FL clients that are heterogeneous in terms of their resources
such as processing capability, memory, bandwidth, battery-life and data
volume. We consider heterogeneous mobile robots for understanding the
effects of real-world FL setting in presence of resource-constrained agents.
We consider an agent unreliable if it repeatedly gives slow response or
infuses incorrect models during training. By disregarding the unreliable
and weak agents, we carry-out the local training of the FL process on
selected agents. If somehow, a weak agent is selected and started showing
straggler issues, we leverage asynchronous FL mechanism that aggregate
the local models whenever it receives a model update from the agents.
Asynchronous FL eliminates the issue of waiting for a long time to receive
model updates from the weak agents. To the end, we simulate how we can
track the behavior of the agents through a reward-punishment scheme
and present the influence of unreliable and resource-constrained agents
in the FL process. We found that FL performs slightly worse than centralized models, if there is no unreliable and resource-constrained agent. However, as the number of malicious and straggler clients increases, our
proposed model performs more effectively by identifying and avoiding
those agents while recognizing human activities as compared to the stateof-the-art FL and ML approaches.
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This content will become publicly available on November 1, 2024
Latency-Aware Semi-Synchronous Client Selection and Model Aggregation for Wireless Federated Learning
Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in the context of various applications, e.g., next-word prediction and eHealth. FL involves various clients participating in the training process by uploading their local models to an FL server in each global iteration. The server aggregates these models to update a global model. The traditional FL process may encounter bottlenecks, known as the straggler problem, where slower clients delay the overall training time. This paper introduces the Latency-awarE Semi-synchronous client Selection and mOdel aggregation for federated learNing (LESSON) method. LESSON allows clients to participate at different frequencies: faster clients contribute more frequently, therefore mitigating the straggler problem and expediting convergence. Moreover, LESSON provides a tunable trade-off between model accuracy and convergence rate by setting varying deadlines. Simulation results show that LESSON outperforms two baseline methods, namely FedAvg and FedCS, in terms of convergence speed and maintains higher model accuracy compared to FedCS.
more » « less- Award ID(s):
- 2148178
- NSF-PAR ID:
- 10490189
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Future Internet
- Volume:
- 15
- Issue:
- 11
- ISSN:
- 1999-5903
- Page Range / eLocation ID:
- 352
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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