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Enabling Efficient Client Selection in FL-as-a-Service for Multi-Application based Society 5.0
Prachi Nandi, Sonakshi Satpathy, Timam Ghosh, Arijit Roy
TL;DR
FLaaS aims to support collaboration across applications, while the paper identifies security, privacy, and hierarchical-architecture challenges in IoT. It uses greedy client selection based on delay, energy consumption, and link status, and reports that Greedy outperforms QL and Random across evaluated metrics.
Problem
FLaaS faces unresolved cross-application security and permission mechanisms, privacy concerns, and a lack of an optimal hierarchical architecture.
Source: §I · p. 1Method
The paper uses a greedy node-selection approach based on delay, energy consumption, and link status to optimize model processing and resource utilization.
Source: §VII · p. 6Results
Greedy consistently outperforms QL and Random across all evaluated metrics.
Source: D. Results · p. 6Takeaways & Limitations
The conclusion presents greedy node selection as effective for optimizing model processing and resource utilization in FLaaS for IoT.
Source: §VII · p. 6The conclusion proposes data heterogeneity as an additional parameter for future research beyond the factors used in the selection strategy.
Source: §VII · p. 6
Abstract
from arXiv · showhide
The rapid development of the Internet of Things (IoT) has led to the generation of vast amounts of data from sensors, prompting the need for advanced learning models to analyze this data for personalized services. Federated learning (FL) emerges as a solution, offering decentralized learning that preserves user privacy by building models on local devices and sharing only aggregated insights. This paper explores FL-as-a-service (FLaaS) in IoT, highlighting its potential for collaborative learning across applications while addressing challenges like security, privacy, and optimizing hierarchical architectures for efficient model convergence and accuracy. To enhance the effectiveness of FL in IoT environments, this study focuses on selecting optimal nodes for model processing through the evaluation of parameters such as delay, energy consumption, and link status. The proposed method aims to identify suitable client nodes for model training. Performance evaluation is conducted using a Human Activity Recognition dataset under simulated IoT network conditions. The proposed approach is compared against randomized and Q-learning based client selection strategies. Experimental results shows improvements in delay and energy efficiency while maintaining communication performance. The findings highlight the potential of efficient client selection mechanisms for enhancing FLaaS in dynamic IoT ecosystems and supporting future intelligent services in Society 5.0.
I. INTRODUCTION
The introduction motivates multi-application FLaaS as a way to support personalized IoT services while identifying unresolved security, privacy, and architecture challenges. The paper frames client selection against related work and models clients’ computation tasks in a client–access-point–cloud system.
I. INTRODUCTION
- FLaaS lets third-party applications collaborate on joint FL models and APIs for personalized needs, but cross-application security, privacy, and hierarchical-architecture challenges remain.The paper identifies permission and data-sharing concerns and notes that differential-privacy approaches can affect convergence and utility.Source: §I · p. 1
II. RELATED WORK
- Prior client-selection work addresses network constraints, reliability, and scalability; this paper positions its algorithm for reliability, resource, and scalability challenges in multi-application FLaaS.The section also notes that client choice affects model quality, training speed, and resource utilization.Source: §II · pp. 1–2
III. SYSTEM MODEL
- The system model includes client devices, access points, and a centralized cloud, with each client executing one task parameterized by compute demand, update size, local iterations, and dataset size.The task parameters are CPU cycles per sample, local-model update size, number of local training iterations, and number of local data samples.Source: §III · p. 2
A. Delay Model
The delay model represents client selection and accounts for local execution and model transmission along the client-to-cloud path. It uses an existing access-point association policy and includes propagation delay in its delay cost.
A. Delay Model
- Client selection is represented by a binary decision parameter, and latency-based selection depends on local computation time and transmission delay.The decision parameter is 1 for a selected client and 0 otherwise.Source: A. Delay Model · p. 2
- The model accounts for transmitting each local model to its associated access point using FDMA, with the achievable rate depending on allocated bandwidth, channel gain, transmit power, and noise.An existing association policy X determines the access point associated with each client.Source: A. Delay Model · p. 2
- Link selection for latency accounts for propagation delay between the access point and cloud, and the model defines a delay cost function from its component delays.The passage describes propagation delay for offloading a task and then introduces the delay-model cost function.Source: A. Delay Model · pp. 2–3
B. Energy Consumption Model
The energy-consumption model considers energy for local FL-model computation and for offloading the model. It then defines a cost function from those energy components.
B. Energy Consumption Model
- The model includes the energy required for local computation of the client’s FL model.The effective switch capacitance of the chip is identified as κ.Source: B. Energy Consumption Model · p. 3
- Offloading energy is the energy a client device uses while transmitting its local model.The passage presents this as the energy required for offloading the task.Source: B. Energy Consumption Model · p. 3
- The model defines an energy-consumption cost function from the local-computation and transmission-energy components.The function is introduced as combining the preceding energy terms.Source: B. Energy Consumption Model · p. 3
C. Link Model
The link model considers packet loss, congestion, and noise when assessing links between access points and the cloud. It estimates congestion from RTT and introduces a cost function based on the link-model quantities.
C. Link Model
- The link model considers traffic congestion, packet loss rate, and noise when assessing a client’s link between an access point and the cloud.The packet-loss passage refers to transmitted and received packet counts between the access point and cloud.Source: C. Link Model · p. 3
- Congestion probability is modeled as linearly related to real-time RTT over the interval from Rmin to Rmax.The passage defines the change in congestion probability range as Δpmax = pmax − pmin and addresses the case RTTmax = RTTmin.Source: C. Link Model · p. 3
- The section introduces a cost function from its link-model equations.The passage labels this as a cost function for the energy-consumption model.Source: C. Link Model · p. 3
A. Problem Formulation
The client-selection problem minimizes an objective that combines delay, link reliability, energy, and entropy-related terms under resource constraints. The paper states that the resulting optimization problem is NP-hard.
A. Problem Formulation
- The objective minimizes a formula to maximize efficiency while factoring entropy and client-selection parameters.The formulation uses delay, energy, and link-reliability cost functions, with constraints on bandwidth, computation capacity, and transmission power.Source: A. Problem Formulation · p. 3
- The minimized FL client-selection problem is NP-hard.The proof identifies the formulation as a multiple integer noninteger linear program (MINLP).Source: A. Problem Formulation · p. 3
- The proof reduces the problem by first assuming an optimum client-to-cloud path, then an optimum device on that path.Source: A. Problem Formulation · p. 3
B. Link Optimization
The link and device optimization formulations treat selected network or device quantities as constants under stated assumptions. For device selection, a decentralized greedy procedure compares client costs and returns task-level acceptance decisions.
B. Link Optimization
- The link model assumes a shortest client-to-cloud path with no packet loss or transmission or propagation latency, making bandwidth and maximum delay constants.Source: B. Link Optimization · p. 3
- The formulation sets C = α1Jτ(x, y) + α3Jθ(x, y), with binary xkj and yk variables, 0 ≤ fk ≤ fmax_k, 0 ≤ pk ≤ pmax_k, and 0 ≤ Jδ(x, y) ≤ Emax_k.Source: B. Link Optimization · p. 3
C. Device Optimization
- The device model assumes minimum energy and lowest entropy, treating transmission power, CPU frequency, and energy as constants.Under these assumptions, the energy and uniform-data models are constant-valued.Source: C. Device Optimization · p. 4
- For each task, clients broadcast delay, energy, link cost, and data entropy; one device compares costs, records acceptances or rejections, and returns the decision to the base station.The approach is described as reducing the search operation to K, the number of client devices.Source: C. Device Optimization · p. 4
V. THEORETICAL ANALYSIS
The theoretical analysis compares delay and energy costs under greedy selection and buffer use. It argues that buffers can reduce delay relative to the stated alternatives, while increased transmission power or CPU frequency increases energy cost.
Theoretical Analysis
- The analysis also states that the delay cost function will not decrease with buffer-node use.Source: §V · p. 4
- For 10 selected nodes, greedy selection chooses low-delay nodes, and the proposed approach states Dk[with] < Dk[without] when buffers are not used.The comparison says the traditional central-node approach increases time complexity.Source: §V · p. 4
- With buffers, the analysis states Dk[with] < Dk[without], arguing that status-check and node-switching delays are outweighed when buffer use is infrequent.The text attributes the small buffer-use probability to greedy selection of the best nodes, which are unlikely to fail.Source: §V · p. 4
- Increasing transmission signal power or CPU frequency increases the energy cost function.The text says energy cost rises as transmission power increases from pk to pmax and CPU frequency from fk to fmax.Source: §V · p. 4
- The greedy decentralized algorithm is reported to have O(K^2) time complexity.The passage describes two loops and two if-else statements inside the for-loop.Source: §V · p. 4
A. Experimental Design
The experimental design evaluates delay and energy for client selection using a smartphone-based Human Activity Recognition dataset and simulated network conditions. It compares random, Q-learning, and greedy selection across groups of 10 nodes.
A. Experimental Design
- The evaluation uses the Human Activity Recognition Using Smartphones dataset and calculates delay and energy under simulated network conditions.A link model uses packet-loss parameters, and randomly selected nodes are analyzed across multiple rounds.Source: A. Experimental Design · p. 4
- Three 10-node selection strategies are compared: random selection using delay and energy, iterative Q-learning, and a greedy algorithm.The passage presents greedy selection as hypothesized to improve performance efficiency and reports its potential for better delay and energy outcomes.Source: A. Experimental Design · p. 5
B. Performance Matrices
Across 40 experiments, the benchmark comparison reports lower delay and energy consumption for Greedy than for Q-Learning and Random selection. The performance matrices also show substantial variation in average delay and energy across experiments.
B. Performance Matrices
- Average delay varies from around 3,000 to over 14,000 across 40 experiments, with peaks in experiments 18, 23, and 30.The study calculates average metrics across 40 experiments and 100 nodes.Source: B. Performance Matrices · p. 5, Figure 1 · p. 5
- Average energy fluctuates between 1.60e+28 and 1.90e+28 across 40 experiments, stabilizing slightly around 1.80e+28 in experiments 15–30.The study evaluates delay, energy consumption, and link status.Source: B. Performance Matrices · p. 5, Figure 2 · p. 5
C. Benchmark Schemes
- Greedy delay is generally below 5,000, versus occasional Q-Learning spikes above 10,000 and Random delays frequently reaching or exceeding 20,000.The comparison covers 40 experiments.Source: C. Benchmark Schemes · pp. 5–6, Figure 3 · p. 6
- Greedy energy stays slightly above 0.5e+28, compared with Q-Learning at approximately 1.3e+28–2.2e+28 and Random at 1.5e+28–3e+28.The plotted comparison covers 40 experiments.Source: C. Benchmark Schemes · p. 6, Figure 4 · p. 6
D. Results
Across the evaluated metrics, Greedy outperforms QL and Random. The conclusion points to data heterogeneity and additional node-selection factors as directions for further refinement.
D. Results
- Greedy achieves the lowest delay across experiments, while QL fluctuates more and Random has the highest, most variable delay.The authors identify Greedy as suitable for latency-sensitive applications.Source: D. Results · p. 6
- Greedy maintains consistently low energy consumption with minimal fluctuations; QL uses slightly more energy, and Random uses the most inconsistently.The authors suggest QL may be considered when slight energy-efficiency trade-offs are acceptable.Source: D. Results · p. 6
- Greedy consistently outperforms QL and Random across delay, energy efficiency, and link quality.QL shows higher variability, while Random is the least favorable choice.Source: D. Results · p. 6
VII. CONCLUSION
- The conclusion recommends evaluating data heterogeneity and exploring reinforcement-learning or hybrid algorithms to improve model robustness, accuracy, and convergence.Data heterogeneity is described as variability in device data distributions; addressing it may support equitable node contributions.Source: §VII · p. 6
- The authors conclude that refining node selection and including more influencing factors can enhance FL’s efficacy for personalized intelligent services in Society 5.0.Source: §VII · p. 7