Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learning

Question

1. What tasks are performed in cloud-edge-terminal IoT networks?

2. What is the state information vector of IoT device M?

3. How does FRL aggregate DNNs in IoT network?

4. What are the key challenges in collaborative policy learning?

Accepted Answer

In cloud-edge-terminal IoT networks, various dynamic scheduling tasks are performed. These tasks include radio resource management, data gathering, and wireless power transfer. Each edge in the network carries out one or more of these tasks, denoted by the set L. The tasks are represented as ( ) L, and the edges involved in a specific task are defined as N ( ) = { : ( ) = }. The network also considers the maximum bandwidth, memory resource, and computing resource available in the cloud server for performing these tasks. Additionally, the system model allows for the extension of scenarios where an edge carries out multiple tasks by conceptualizing the edge as a collection of distinct virtual edges, each representing an individual task.

Accepted Answer

The state information vector of IoT device M in time slot is defined as = ( , ,1 , . . ., , , ( ( ) ) ), where , , represents the th state information of IoT device in time slot, and ( ) is the number of types of state information for task . This vector captures the current conditions and status of the IoT device, which is crucial for effective scheduling decisions. It includes various parameters such as queue length, channel conditions, and other relevant factors that influence the scheduling process. By considering this state information, edges can make informed decisions to optimize the scheduling tasks and achieve the desired goals.

Accepted Answer

FRL aggregates DNNs in IoT network by effectively aggregating the local DNNs (i.e., the local policies) from all edges conducting the task over multiple time slots in each round. The central parameters of the DNN for task at the cloud server are denoted by , and the local parameters of the DNN at edge are denoted by w. The cloud server broadcasts the central parameters, , for task in round to the edges in N ( ). Then, in round, each edge substitutes its local parameters, w , with . After this substitution, each edge trains its local parameters using its local experiences. These trained parameters are then uploaded to the cloud server. The cloud server updates its central parameters for task by aggregating the received parameters from edges in N ( ), using EQUATION. This process repeats for multiple rounds, effectively aggregating the DNNs and solving the problem in (6).

Accepted Answer

The key challenges in collaborative policy learning include limited cloud resources for collaborative policy learning on multiple tasks, and the inapplicability of conventional policy structures. Limited cloud resources require careful task selection to maximize participation while maintaining fairness. The inapplicability of conventional policy structures arises from varying dynamics due to different numbers of IoT devices and system uncertainties, leading to different state and action spaces and transition probabilities. This necessitates a policy structure with generalization capability to collaboratively learn a central policy for all tasks. These challenges will be addressed in Section IV-A and IV-B, respectively.

Accepted Answer

The task selection algorithm in round determines the task selection, q( ), using the formula q( ) = argmax ( ) L, where ( ) is the Lagrange multiplier of task in round with respect to the auxiliary variable, ( ) is the Lagrange multiplier with respect to the constraint in (13), and ( ) is the number of available edges with task in round. At the end of round, the Lagrange multipliers are updated using the formula EQUATION, where ( ) is the positive step size in round, and ( ) = argmax >=0 { ( )- ( ) }. This algorithm optimally solves the dynamic scheduling task selection problem in (14) as demonstrated in Theorem 1. The problem in (18) is a typical multidimensional knapsack problem, which can be solved efficiently using dynamic programming or branch-and-bound methods.

Accepted Answer

The proposed approach for learning the edge-agnostic policy via DRL in edge is based on the system model described in Section II-B. Using the reward function ( ), edge can learn the edge-agnostic policy ( ) that solves the dynamic scheduling problem of edge (and can also be used for other edges with task ( )) via DRL methods. Specifically, a DNN is employed to approximate the optimal action-value function, based on the edge-agnostic states and actions. The DNN structure for the edge-agnostic policy is determined based on task ( ), and all edges associated with the same task have an identical DNN structure. The optimal action-value function with a given - ( ) and - ( ) is denoted by - * ( ) ( - ( ) , - ( ) ), while its Q-approximation derived from the DNN is denoted by - ( ) ( - ( ) , - ( ) ; w ). In time slot , the observed state in accordance with ( 1) is translated into the edge-agnostic state - ( ) as per (19) . Based on - ( ) , the edge-agnostic policy chooses the edge-agnostic action - ( ) from A ( - ), according to its exploration-exploitation strategy (for instance, an -greedy method). Subsequently, the selected edge-agnostic action - ( ) in line with (20) is translated into the action as per (2) . When more than one IoT device fulfills the condition indicated by the edge-agnostic action, one of these IoT devices is arbitrary selected as the scheduled IoT device for that time slot. The translation of states and actions is illustrated in Fig. 2. After scheduling, the reward ( ) ( , ) and the next state +1 are observed. Then, an edge-agnostic experience sample for time slot is generated as ( - ( ) , - ( ) , ( ) , - +1 ( ) . Using these experience samples, the DNN is trained in line with standard DQN methods, incorporating experience replay and fixed-target Q-network..

Accepted Answer

The collaborative policy learning framework for dynamic scheduling tasks in IoT networks leverages FRL. It involves a task selection algorithm and a collaborative learning-applicable scheduling policy. Both the cloud server and each edge initialize their DNNs to learn an edge-agnostic policy. The cloud server initializes DNN parameters as 1, L, and (1), while each edge initializes local parameters as 1 (). The framework includes a procedure where the cloud server observes x (), obtains the task selection decision q (), and runs FEDDS(, x ()). In parallel, the cloud server and edges update their Lagrange multipliers and temporarily pause their DQNs. This framework enables efficient dynamic scheduling tasks in IoT networks.

Accepted Answer

DQN algorithms operate concurrently by allowing edges to execute their DQN algorithms with local parameters, w, as described in Section IV-B2. These algorithms can be temporarily suspended to accommodate FRL. During round, the cloud server evaluates the availability of edges for FRL and makes a task selection decision. For each selected task, the cloud server and available edges conduct FRL in parallel. Available edges temporarily suspend their DQN algorithms to maintain current local parameters, w. They calculate local gradients using local parameters and upload them to the cloud server. The cloud server computes central parameters for the task and broadcasts them to all edges. Edges substitute their locally trained parameters with central parameters and set their local parameters for the next round. Once FRL concludes, the cloud server updates Lagrange multipliers for tasks to ensure fairness.

Accepted Answer

The assumptions made in the convergence analysis of collaborative policy learning are: 1) The objective function of FL (w) is smooth, with a Lipschitz continuous gradient. 2) The objective function of FL (w) is strongly convex. 3) The variance of the gradients at each edge is bounded for all rounds. 4) The expected squared norm of the gradients at each edge is uniformly bounded for all rounds. Additionally, a parameter G is introduced to represent the degree of experience distribution difference for each edge. These assumptions help capture and quantify the non-independent and identically distributed experiences among edges, leading to the convergence of the collaborative policy learning framework for dynamic scheduling tasks.

Accepted Answer

The three tasks evaluated in the experimental results section are Task A, Task B, and Task C. Task A aims to minimize power outages of IoT devices attributable to low battery levels by wirelessly transferring power from an Access Point (AP) to a selected IoT device. Task B aims to maximize the number of gathered data samples while minimizing dropped data samples in an IoT network. Task C aims to minimize the transmission power at an AP while ensuring the minimum average data rate requirements of IoT devices. Each task has specific objectives and state information used to determine the cost, reward, and performance of the proposed collaborative policy learning framework.

Accepted Answer

The FL-PF algorithm selects tasks in a manner that promotes effective collaborative policy learning, considering the time-varying availability conditions of edges and limited resources. It consistently meets the minimum number of participants across all tasks. In contrast, FL-Greedy excessively selects task A in nearly every round, resulting in a participant count close to that of Bench. However, it falls short of the minimum for tasks B and C due to its skewed selection towards task A. FL-RR selects tasks in a circularly fair manner but does not take the number of participants into account, leading to fluctuating participant counts that depend on the arrival rate of each task. This imbalance creates unfairness among the tasks and may result in tasks B and C not achieving enough performance improvement from collaborative policy learning.

Accepted Answer

Collaborative policy learning algorithms, such as FL-PF, FL-RR, and FL-Greedy, exhibit superior performance compared to No-FL. FL-PF outperforms FL-RR and FL-Greedy, closely matching the performance of Bench. All algorithms yield similar rewards, significantly exceeding that of No-FL. FL-Greedy secures an average reward almost identical to Bench. FL-PF surpasses both FL-RR and FL-Greedy, achieving a reward close to Bench. The number of participants and reward follow similar trends, with FL-PF reaping larger rewards for tasks B and C compared to FL-RR, and achieving higher participant numbers. FL-Greedy secures rewards nearly equal to Bench for task A with many participants, but comparable to No-FL for tasks B and C with fewer participants. These findings suggest that fairness among tasks, in terms of participant numbers, should be considered for performance improvement in collaborative policy learning.

Accepted Answer

Collaborative policy learning effectively manages unseen edge arrivals by immediately utilizing the task policy located at the cloud server. This approach prevents reward degradation and performance issues that arise when a new edge with a task arrives. Unlike No-FL, which requires learning a new policy for the newly arrived edge, collaborative policy learning can adapt quickly to dynamic edge arrivals in realistic IoT networks. The framework's ability to handle unforeseen edge arrivals is demonstrated through simulations where four edges are introduced after 25,000 time slots, with two associated with scenario D and two with scenario E. The results show that FL-PF, chosen as the representative algorithm, maintains stable rewards and outperforms No-FL in scenarios with new edge arrivals. This highlights the effectiveness of collaborative policy learning in IoT networks, where policies may encounter novel scenarios without prior experience.

Accepted Answer

The number of edges significantly affects the learning speeds of FL-PF and No-FL. In Fig. 8a, it is evident that FL-PF learns faster than No-FL, especially as the number of edges increases. This is due to the collaborative policy learning in FL-PF, which allows it to capitalize on the collective experiences of multiple edges. On the other hand, No-FL's learning speed does not show a clear trend with the number of edges, as each edge must rely solely on its own experience to learn a policy. This demonstrates the efficacy of collaborative policy learning in FL-PF when there are more edges.

Accepted Answer

The key enabler of the proposed framework is the edgeagnostic policy structure, which is applicable to collaborative learning in dynamic scheduling tasks. This policy structure allows for effective utilization of limited cloud resources while ensuring fair local policy aggregation across tasks. It is designed to be adaptable to newly arrived edges and accelerate the learning speed of the policy. The edgeagnostic policy structure plays a crucial role in achieving the best performance when combined with the proposed task selection algorithm, as demonstrated by the experimental results in the paper. Overall, this policy structure contributes to the effectiveness and efficiency of the collaborative policy learning framework for IoT networks using FRL.

Accepted Answer

The wireless power transfer rate in IoT devices, denoted by h, depends on the wireless channel condition at a given time slot. This condition typically varies with time. Additionally, the active state of the IoT device, represented by 1 for active and 0 for inactive, influences the charging rate. The active state probabilistically changes based on a Markov model, with state transition probabilities set to 0.5 for both active to inactive and inactive to active transitions. The battery level of the IoT device is updated according to its active state and wireless power transfer from the AP, using the equation provided in the section. Therefore, factors such as wireless channel condition, active state, and charging rate h play crucial roles in determining the wireless power transfer rate in IoT devices.

Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learning

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Most frequently asked questions

1. What tasks are performed in cloud-edge-terminal IoT networks?

2. What is the state information vector of IoT device M?

3. How does FRL aggregate DNNs in IoT network?

4. What are the key challenges in collaborative policy learning?

5. What is the task selection algorithm in round?

6. What is the proposed approach for learning the edge-agnostic policy via DRL in edge?

7. What is the collaborative policy learning framework for dynamic scheduling tasks?

8. How do DQN algorithms operate concurrently?

9. What assumptions are made in the convergence analysis of collaborative policy learning?

10. What are the three tasks evaluated in the experimental results section and their objectives?

11. How do FL-PF, FL-Greedy, and FL-RR algorithms compare in terms of participant selection?

12. How do collaborative policy learning algorithms compare to No-FL?

13. How does collaborative policy learning handle unseen edge arrivals?

14. How does the number of edges impact FL-PF and No-FL learning speeds?

15. What is the key enabler of the proposed framework?

16. What factors affect the wireless power transfer rate in IoT devices?

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