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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Scheduling Policy and Power Allocation for Federated Learning in NOMA based MEC
Federated learning (FL) is a highly pursued machine learning technique that can train a model centrally while keeping data distributed. Distributed computation makes FL attractive for bandwidth limited applications especially in wireless communications. There can be a large number of distributed edge devices connected to a central parameter server (PS) and iteratively download/upload data from/to the PS. Due to limited bandwidth, only a subset of connected devices can be scheduled in each round. There are usually millions of parameters in the state-of-art machine learning models such as deep learning, resulting in a high computation complexity as well as a high communication burden on collecting/distributing data for training. To improve communication efficiency and make the training model converge faster, we propose a new scheduling policy and power allocation scheme using non-orthogonal multiple access (NOMA) settings to maximize the weighted sum data rate under practical constraints during the entire learning process. NOMA allows multiple users to transmit on the same channel simultaneously. The user scheduling problem is transformed into a maximum-weight independent set problem that can be solved using graph theory. Simulation results show that the proposed scheduling and power allocation scheme can help achieve a higher FL testing accuracy in NOMA based wireless networks than other existing schemes within the same learning time.
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- Award ID(s):
- 2007995
- NSF-PAR ID:
- 10295626
- Date Published:
- Journal Name:
- IEEE Globecom 2020
- Page Range / eLocation ID:
- 1 to 7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/GLOBECOM42002.2020.9322270
