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REVIEW 3 major objections 5 minor 39 references

Generalising Battery Control in Net-Zero Buildings via Personalised Federated RL

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Federated TRPO without tuning is claimed to match tuned PPO for net-zero battery control.

desk verdict The headline claim contradicts the paper's own tables; the useful toy benchmark and honest experiments still merit a serious referee. read the letter →

arxiv 2412.20946 v2 pith:DDMGWCZS submitted 2024-12-30 cs.LG

Nicolas M Cuadrado Avila , Samuel Horváth , Martin Takáč This is my paper · ORCID
classification cs.LG
keywords federatedreinforcementlearningbatterycontrolmicrogridsnet-zerobuildingsPPOTRPOCityLearnprivacy-preserving
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to show that a collaborative, privacy-preserving reinforcement learning framework can learn effective battery-charging policies for net-zero buildings. It argues that an untuned Trust Region Policy Optimization (TRPO) can perform on par with a tuned Proximal Policy Optimization (PPO) when trained across multiple buildings using a federated-style averaging scheme and split-learning personalization. The authors build a customized CityLearn environment where self-sustainability is achievable by design, so they know the optimal policy in advance and can measure the optimality gap. The central goal is to demonstrate that federated learning enables generalization across heterogeneous buildings while protecting occupant data, and that even simple net-zero policies are surprisingly hard to learn.

What carries the argument

The mechanism carrying the argument is a federated learning loop in which each building's agent samples its own trajectories and the gradients are averaged across agents, which the authors equate to FedAvg aggregation, plus a split-learning personalization block (an optional private encoding of household-specific features) that is not shared. TRPO's constrained, second-order policy updates provide stable, adaptive step sizes without manual tuning, while PPO's clipped surrogate serves as the tuned baseline. The environment is a stripped-down CityLearn scenario with synthetic data engineered so that solar generation plus battery storage can cover each home's load completely, making the optimal policy known (zero cost and zero emissions).

What would settle it

Run the same experiment in a true federated setting: each building trains its own model on its own local data for several local epochs, then sends only model weights to a central server for averaging, with no direct sharing of observations across buildings, and compare the evaluation reward against the paper's stacked-gradient results; if true federated TRPO does not match a tuned PPO, the central claim is refuted.

Watch

Extended reading notes

Core claim

The paper's central claim is that Federated TRPO, combined with FedAvg-style aggregation and a personal encoding block kept private to each building, is comparable with state-of-the-art federated RL methodologies without hyperparameter tuning. In the authors' experiments, a tuned PPO generally achieves higher rewards and lower cost and emissions, but TRPO converges reliably without tuning and consistently beats the no-battery baseline in both two-building and five-building microgrid scenarios. The paper further reports that grouping features and personal encoding help TRPO in the shifted (more heterogeneous) datasets. The authors conclude that even a simple optimal policy—charge when solar is available, discharge when needed—is hard to learn, and they suggest a pragmatic warm-start strategy: a few PPO iterations followed by TRPO.

Load-bearing premise

The paper assumes that training a single network on observations stacked from all buildings is equivalent to doing actual federated averaging of locally trained models, and the entire 'federated' claim falls if that equivalence does not hold in practice.

Editorial extensions

If this is right

  • If the paper is right, an untuned TRPO can be a drop-in replacement for tuned PPO in federated building-control applications, saving the effort of hyperparameter search.
  • The demonstrated generalization across buildings suggests that privacy-preserving federated learning can pool knowledge across heterogeneous sites and still approach net-zero operation.
  • The finding that simple optimal policies are hard to learn supports curriculum-style training that starts with well-controlled toy scenarios before scaling to real-world complexity.
  • Warm-starting with a few PPO iterations and then switching to TRPO could yield a practical hybrid algorithm for battery control.
  • The synthetic-data testbed with a known optimal policy offers a diagnostic for measuring optimality gaps in RL algorithms beyond this specific application.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A reader should note that the equivalence between the paper's stacked-observation setup and a true federated protocol (with local training steps and communication rounds) is assumed, not tested, so the 'federated' results may not transfer directly to real federated deployments with communication constraints or privacy noise.
  • The reported tables show a large reward gap between tuned PPO and untuned TRPO; the claim of parity may depend on the definition of 'comparable,' and a fair test would tune TRPO as well or hold both methods to the same tuning budget.
  • The synthetic scenario's design—where net-zero is achievable but difficult to learn—could be reused as a benchmark for diagnosing convergence and optimality of other RL algorithms, especially second-order methods.
  • If the stacked-gradient approximation is found to be inaccurate, the paper's conclusions about federated TRPO would need to be re-verified under a real federated averaging scheme, which is a straightforward extension.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper studies battery control in net-zero building microgrids using a customized CityLearn environment with synthetically generated data and a known optimal policy. It compares PPO and TRPO under several collaborative configurations (shallow network, personal encoding, grouped features, and shifted data) and reports that an untuned Federated TRPO is comparable with a tuned federated PPO. The authors also contribute a TorchRL wrapper for CityLearn and release code.

Significance. If the central claim were supported, the paper would offer a useful practical lesson: a hyperparameter-free TRPO could match a tuned PPO in a privacy-preserving shared-control setting for building energy management. The paper has concrete strengths: a reproducible synthetic benchmark with a known optimal policy, five-seed experiments, public code, and a modular environment wrapper. However, the headline claim is directly contradicted by the paper's own quantitative tables, and the experiments do not actually implement a federated protocol. The significance of the work as a federated RL contribution is therefore not established.

major comments (3)
  1. [Abstract, Section 4.2, Tables 1 and 2, Section 5] The central claim that Federated TRPO is comparable with tuned federated PPO is contradicted by the paper's own results. In Table 1, the best TRPO evaluation reward is -0.47064 (pe) while the best PPO evaluation reward is -0.14021 (pe gf); in Table 2, the corresponding values are -0.45768 (base) for TRPO and -0.14662 (pe gf) for PPO. Section 4.2 explicitly states that "the performance of the tuned PPO was superior to that of the TRPO." No standard deviations or statistical tests are reported for these five-seed averages, so the conclusion "Federated TRPO performed on par with a tuned federated PPO" has no statistical or numerical basis in the presented data.
  2. [Appendix A.2] The paper argues that "passing the stacked observations to a single network has the same effect as doing a FedAvg," but this equivalence is not established and is generally false. FedAvg involves multiple local updates between communication rounds, client sampling, and aggregation of model weights; a single forward/backward pass over a concatenated batch of agent observations is a centralized mini-batch update, not a federated protocol with local training steps. Since no communication rounds, partial participation, or privacy mechanism are implemented or analyzed, the claims that the framework is "privacy-preserving" and that the results concern "Federated TRPO" are not supported by the experiments.
  3. [Abstract and Section 5] The abstract claims comparability with "state-of-the-art federated RL methodologies," but the paper contains no comparison with any external federated RL baseline. All comparisons are among PPO and TRPO variants within the proposed setup. The conclusion's additional recommendation to warm-start with PPO and switch to TRPO is also not tested anywhere in the experiments.
minor comments (5)
  1. [Tables 1 and 2] The tables report means over five seeds but no standard deviations or confidence intervals, even though the accompanying figures show substantial variance; please add dispersion measures to the tables.
  2. [Table 2 caption] The caption reads "Values in the table represent the average for 5" and appears to be missing the word "seeds."
  3. [Section 4.2] The sentence "PPO overperforms TRPO significantly, but we highlight that we didn't spend time fine-tuning it" is ambiguous about whether "it" refers to TRPO or PPO; Appendix C indicates PPO was tuned while TRPO was not, so please rephrase.
  4. [Appendix B] The description of the selling-price margin as "a margin of 40% of the lowest daily electricity pricing" is unclear; specify exactly how the selling price is computed from the daily pricing series.
  5. [Section 3.2 and Appendix A.2] Figure 1 labels the Personal Encoding block as a local component, but Appendix A.2 states that "we are not in the process of training a private part of the policy network" and uses a one-hot encoder. Please clarify what the personal encoding block actually contains and how it relates to split learning.

Circularity Check

1 steps flagged · score 2.0 of 10

No derivation-chain circularity; the 'Federated' claim is self-definitional because Appendix A.2 equates centralized stacked training with FedAvg.

  1. self definitional [Appendix A.2 (Federated RL)]
    "Passing the stacked observations to a single network has the same effect as doing a FedAvg: each agent samples their trajectories, and after a forward pass, the backward pass will propagate the average gradient of all of them, which is, in practice, the same behavior we expect from the FL methodology."

    The advertised central result is about 'Federated TRPO'. Appendix A.2 defines the experimental setup by equating a single network seeing stacked observations with FedAvg, and explicitly states 'we are not required to implement the exact flow of FL'. Hence every 'Federated TRPO' performance number is, by the paper's own stipulation, a centralized single-network number. The conclusion 'Federated TRPO performed on par with a tuned federated PPO' therefore reduces to 'our centralized stacked-network TRPO performed on par with our centralized stacked-network PPO'—the very experiments that were run—so the 'federated' label carries no independent empirical content.

full rationale

Strictly, this paper contains no mathematical derivation chain whose outputs are equivalent to its inputs by construction. The 'known optimal policy' is an external benchmark built from the synthetic-data rule, not fitted from the agents' rewards, and the comparisons to tuned PPO, while internally inconsistent with the 'on par' sentence (Tables 1-2 show TRPO eval rewards around -0.47 vs PPO around -0.14), are an accuracy/consistency problem, not a circular one. The one genuinely self-referential step is Appendix A.2, where the paper stipulates that passing stacked observations to one network 'has the same effect as doing a FedAvg'. This is load-bearing for every 'Federated TRPO' statement: the federated label is attached by definition rather than by running a distributed protocol, so results labeled 'federated' are, by the paper's own equivalence, results about a centralized single network. I count this as a minor self-definitional labeling issue, not as a derivation that is forced by construction; the underlying empirical measurements remain self-contained. No load-bearing self-citations or imported uniqueness theorems appear.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central result rests on a synthetic environment designed so the optimal policy is known, and on an approximation of federated averaging by centralized stacking. Both are declared in the paper but limit the scope of the claims: the experiments show how collaborative training behaves in a simplified simulator, not how a real federated protocol would behave with privacy constraints.

free parameters (3)
  • Reward weights W_C and W_G = 0.4 and 0.6
    Set by the authors to prioritize emissions over cost; every reported cost, emission, and reward value depends on these weights (Section 3.1).
  • Selling-price margin = 40% of the lowest daily electricity price
    Introduced in Appendix B to prevent profitable arbitrage between buying and selling prices; this changes the optimal policy and the difficulty of the task.
  • PPO hyperparameters = not specified in the text
    Found by Wandb Bayesian sweeps on the evaluation reward (Appendix C); only PPO is tuned, making the comparison with untuned TRPO asymmetric.
assumptions (4)
  • ad hoc to paper Centralized stacking of all agents' observations through one shared network has the same effect as FedAvg.
    Stated in Appendix A.2 and used to call the experiments federated without implementing local update steps, communication rounds, or privacy guarantees.
  • domain assumption The synthetically generated solar and load profiles guarantee net-zero feasibility, so the known optimal policy has zero cost and zero emissions.
    Used in Section 3.1 and Appendix B as the oracle against which all policies are scored.
  • domain assumption Batteries do not degrade, so the optimal policy is a simple daily charge and discharge cycle.
    Stated in Section 3.1 as a simplification; it makes the environment tractable but limits transfer to real storage systems.
  • domain assumption The reward function with weights W_C and W_G plus the infeasibility penalty fully captures the real objective.
    Defined in Table 3; this reward design determines what the agents optimize and what the reported numbers mean.

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Cite this review

Pith. "Pith review of Generalising Battery Control in Net-Zero Buildings via Personalised Federated RL." pith.science (2026). https://pith.science/paper/DDMGWCZS

@misc{pith2026241220946,
  author       = {Pith},
  title        = {Pith review of: Generalising Battery Control in Net-Zero Buildings via Personalised Federated RL},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DDMGWCZS}},
  note         = {Machine review of arXiv:2412.20946}
}
read the original abstract

This work studies the challenge of optimal energy management in building-based microgrids through a collaborative and privacy-preserving framework. We evaluated two common RL algorithms (PPO and TRPO) in different collaborative setups to manage distributed energy resources (DERs) efficiently. Using a customized version of the CityLearn environment and synthetically generated data, we simulate and design net-zero energy scenarios for microgrids composed of multiple buildings. Our approach emphasizes reducing energy costs and carbon emissions while ensuring privacy. Experimental results demonstrate that Federated TRPO is comparable with state-of-the-art federated RL methodologies without hyperparameter tuning. The proposed framework highlights the feasibility of collaborative learning for achieving optimal control policies in energy systems, advancing the goals of sustainable and efficient smart grids. Our code is accessible \href{https://github.com/Optimization-and-Machine-Learning-Lab/energy_fed_trpo.git}{\textit{this repo}}.

Figures

Figures reproduced from arXiv: 2412.20946 by the authors.

Figure 1
Figure 1. Overview of our models: We use the same type of net￾work for Policy and State-Value networks, changing only the out￾put layer. The Personal Encoding block is optional. 3.2. Model We explore the advantages of a collaborative learning ap￾proach for the case of microgrids, given their distributed nature: each household has an agent that explores and learns from different sections of the state space, given the indi￾vidu… view at source ↗
Figure 2
Figure 2. Optimal Actions and State of Charge: Mean and variance throughout the day, considering a complete year, for a building. By design, we know the optimal policy that must yield zero emissions and cost beforehand. 8 [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Sample of the Mean and Variance of Solar Generation and Non-Shiftable Load over 24 hours for a year of data for one of the buildings. We generated our synthetic data by altering the Non-Shiftable Load time series using a simple rule that defines the policy we want to learn: the households have enough energy and battery capacity to serve their demand when there is solar energy and to store what is necessary when ther… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Mean and Variance of Electricity Pricing (ToU) and the Selling Pricing over 24 hours for a year of data. C. Hyperparameters We perform a hyperparameter search with Wandb sweeps (Biewald, 2020), using Bayesian search to find the best configura￾tions for PPO and TRPO wit…
Figure 5
Figure 5. Figure 5: Best PPO policies: Plots show mean with an envelope of one standard deviation computed from the five different seeds. Increasing the number of buildings makes it harder for the agents to approach net-zero energy and training with the shifted version of the dataset, som…
Figure 6
Figure 6. Figure 6: Best TRPO policies: Plots show mean with an envelope of one standard deviation computed from the five different seeds. some configurations (model with personal encoding and grouping) limited the variance. They beat the baseline on each run, which is a highlight conside…
Figure 7
Figure 7. Figure 7: Best policies for the 2-building environment: Comparison of learned policy against the optimal policy, averaged for the five seeds. In [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.