FedDRM: Disambiguating Federated Optimization

• FedDRM is a term used for multiple federated frameworks, including deep reinforcement learning for energy control, distributionally robust learning, and demand flexibility aggregation.
• Each formulation employs a server-mediated aggregation loop with domain-specific local updates such as SAC-based control, dynamic client-drift regularization, or polyhedral base-set optimization.
• The frameworks prioritize privacy preservation and communication efficiency by exchanging minimal parameters while ensuring robust, scalable distributed optimization.

FedDRM is a label used in recent literature for multiple federated optimization frameworks rather than for a single standardized algorithm. In the current arXiv record, it denotes at least three distinct constructions: a federated deep reinforcement learning framework for smart micro-grid energy control, a distributionally robust federated learning algorithm with client-drift minimization that is also named DRDM, and a federated method for aggregating demand flexibility without exchanging raw user data (Rezazadeh et al., 2022, Krouka et al., 21 May 2025, Dong et al., 23 Sep 2025). The common denominator is a server-mediated learning or aggregation loop over distributed participants, but the mathematical objects, objectives, and performance criteria differ substantially.

1. Terminological scope and research contexts

The name appears in three technically distinct settings. In energy management, it refers to a hierarchical architecture in which local Building Energy Management Systems train discretized soft actor-critic agents and periodically federate model weights through an Energy Management System. In federated optimization, it refers to a distributionally robust min-max procedure that couples a DRO objective to a FedDyn-style dynamic regularizer and periodic dual updates. In flexibility aggregation, it refers to a bilevel-to-single-level federated optimization pipeline for learning a shared base set for demand-side resources (Rezazadeh et al., 2022, Krouka et al., 21 May 2025, Dong et al., 23 Sep 2025).

Usage of “FedDRM”	Domain	Core object
Smart micro-grid energy control	Federated DRL	Federated global model $\omega_G$
FedDRM (a.k.a. DRDM)	Distributionally robust FL	Saddle-point objective $F(w,\lambda)$
Federated aggregation of demand flexibility	Demand flexibility aggregation	Shared base set $\mathcal U_0(\mathbf h_0)$

A recurrent source of confusion is the assumption that the acronym identifies one canonical method. The literature instead shows domain-specific reuse of the name. This suggests that any technical discussion of FedDRM benefits from immediate disambiguation by problem setting, especially when comparing implementations, guarantees, or privacy properties.

2. Hierarchical federated deep reinforcement learning for smart micro-grids

In "A Federated DRL Approach for Smart Micro-Grid Energy Control with Distributed Energy Resources" (Rezazadeh et al., 2022), FedDRM is a two-tier architecture composed of a local layer of BEMS agents and a federation layer hosted by the EMS. Each house $h\in\mathcal H$ embeds a BEMS with a DRL agent. At each discrete time slot $t$, the agent observes local conditions and chooses one of three actions: trade with the external grid, charge the battery, or discharge the battery. The transition $(s_t,a_t,r_t,s_{t+1})$ is stored in a replay buffer. Every $\hat T$ time slots, each BEMS uploads local model weights $\omega_h$ to the EMS, which computes

$$\omega_G=\frac1{|\mathcal H|}\sum_{h=1}^{|\mathcal H|}\omega_h$$

and broadcasts $\omega_G$ back to the BEMS agents.

The local Markov decision process is defined over the state

$F(w,\lambda)$0

where $F(w,\lambda)$1 is PV generation, $F(w,\lambda)$2 is battery state-of-charge, $F(w,\lambda)$3 is ambient temperature, and $F(w,\lambda)$4 is household demand. The action space is $F(w,\lambda)$5, where $F(w,\lambda)$6 trades surplus or shortage with the external grid while the battery is idle, $F(w,\lambda)$7 charges the battery using PV surplus, and $F(w,\lambda)$8 discharges the battery to cover deficit. The reward is

$F(w,\lambda)$9

which encourages maximizing local utilization and minimizing grid imports.

The learning core is a discretized soft actor-critic procedure with a maximum-entropy return

$\mathcal U_0(\mathbf h_0)$0

The implementation maintains two critics with targets, uses the standard SAC critic loss, stochastic-gradient policy update, and temperature update, and handles discrete actions by replacing continuous reparameterization with enumeration over $\mathcal U_0(\mathbf h_0)$1 and a soft-max policy. The local training loop initializes $\mathcal U_0(\mathbf h_0)$2 and a replay buffer, performs local SAC updates, and triggers upload and replacement by $\mathcal U_0(\mathbf h_0)$3 whenever $\mathcal U_0(\mathbf h_0)$4.

The reported setup uses Intel Xeon Gold 5218, NVIDIA RTX 2080 Ti, and TensorFlow 2.5; data consist of 3-year real smart-house traces at 5 min resolution for consumption, PV production, and temperature; the scenario includes up to $\mathcal U_0(\mathbf h_0)$5 houses, each with a 10 kWh battery. Evaluation metrics are cumulative reward as a cost proxy, CO$\mathcal U_0(\mathbf h_0)$6 emissions in kg CO$\mathcal U_0(\mathbf h_0)$7/kWh, convergence speed in episodes, and battery charge cycles. Baselines are DQN, DDPG, and random.

The quantitative results identify SAC as the strongest single-house DRL baseline: it converges to the highest cumulative reward, outperforming DDPG by approximately $\mathcal U_0(\mathbf h_0)$8, and DQN and random by much larger margins. In CO$\mathcal U_0(\mathbf h_0)$9 terms, SAC emits $h\in\mathcal H$0 kg CO$h\in\mathcal H$1/kWh versus $h\in\mathcal H$2 for DQN and $h\in\mathcal H$3 for random, with $h\in\mathcal H$4 in a paired $h\in\mathcal H$5-test over 10 runs. It also maintains state-of-charge above the $h\in\mathcal H$6 threshold $h\in\mathcal H$7 of the time. In the federated regime, FedDRM with 20 houses reaches stable average reward after approximately $h\in\mathcal H$8k steps, whereas local SAC saturates earlier with lower reward; the paired $h\in\mathcal H$9-test over ten seeds gives $t$0. The paper further reports communication cost growing linearly with $t$1, while exchanging only model weights every $t$2 slots keeps bandwidth low at approximately $t$3 MB per update per house.

The framework is explicitly motivated by distributed energy coordination under privacy constraints. Only neural-network parameters are exchanged, while raw consumption and generation data remain local. Within that formulation, privacy preservation is not an auxiliary feature but part of the system design.

3. Distributionally robust federated learning with client-drift minimization

In "Distributionally Robust Federated Learning with Client Drift Minimization" (Krouka et al., 21 May 2025), FedDRM is presented as DRDM and addresses heterogeneous federated learning through a distributionally robust objective and dynamic regularization. Standard federated learning minimizes

$t$4

where $t$5. The robust alternative replaces the client average with the worst-case mixture over client distributions. With

$t$6

the learning problem becomes

$t$7

The drift-mitigation mechanism follows a FedDyn-style penalty. At communication round $t$8, client $t$9 maintains a control-variates vector $(s_t,a_t,r_t,s_{t+1})$0, initialized at zero. On receiving the global model $(s_t,a_t,r_t,s_{t+1})$1, client $(s_t,a_t,r_t,s_{t+1})$2 approximately solves a modified local problem with regularizer

$(s_t,a_t,r_t,s_{t+1})$3

After $(s_t,a_t,r_t,s_{t+1})$4 stochastic-gradient steps, the client updates the drift-corrector as

$(s_t,a_t,r_t,s_{t+1})$5

At each round, the server samples a participation set $(s_t,a_t,r_t,s_{t+1})$6 of $(s_t,a_t,r_t,s_{t+1})$7 clients with probabilities proportional to $(s_t,a_t,r_t,s_{t+1})$8, broadcasts $(s_t,a_t,r_t,s_{t+1})$9, and aggregates updated local models and randomly chosen snapshot iterates. It then performs a dual update by estimating $\hat T$0 from a fresh sampled set $\hat T$1 and projecting

$\hat T$2

The convergence analysis assumes $\hat T$3-smoothness, bounded gradients, bounded stochastic variance, bounded domains, and a gradient-dissimilarity parameter $\hat T$4. Under convexity of each $\hat T$5 and linearity of $\hat T$6 in $\hat T$7, Theorem 1 chooses

$\hat T$8

and establishes an $\hat T$9 saddle-point guarantee. The proof sketch proceeds through virtual iterates, one-step progress bounds, drift-control lemmas, and telescoping over local and communication steps, with the stated rate arising from balancing infrequent $\omega_h$0-updates against stochastic-gradient errors.

The experiments use 30 clients, per-round participation $\omega_h$1, local steps $\omega_h$2, and 10 Monte-Carlo trials. Datasets are MNIST with a linear model, Fashion-MNIST by fine-tuning the last layer of a pre-trained ResNet-18, and Kuzushiji-MNIST with a small CNN of two conv+pool layers plus a fully connected layer. Heterogeneity is induced by Zipf-distributed local sample sizes with $\omega_h$3 and Dirichlet class splits with concentration $\omega_h$4. Baselines are FedAvg, DRFA, SCAFFOLD, and SCAFF-PD. Metrics are average test accuracy, worst-case client test accuracy, standard deviation across clients, and an energy model

$\omega_h$5

Across all heterogeneity settings and model architectures, DRDM is reported to achieve the highest average and worst-case accuracies and the lowest inter-client standard deviation. Relative to DRFA and FedAvg, it typically reduces the number of communication rounds required to reach a given worst-case accuracy by $\omega_h$6–$\omega_h$7. The energy analysis shows that larger $\omega_h$8 reduces required rounds but increases local computation. Under low-SNR and narrow-bandwidth conditions, transmission dominates and the optimal $\omega_h$9 is approximately $$\omega_G=\frac1{|\mathcal H|}\sum_{h=1}^{|\mathcal H|}\omega_h$$0–$$\omega_G=\frac1{|\mathcal H|}\sum_{h=1}^{|\mathcal H|}\omega_h$$1; under high-SNR at $$\omega_G=\frac1{|\mathcal H|}\sum_{h=1}^{|\mathcal H|}\omega_h$$2 dB and wide bandwidth, computation dominates and the optimal $$\omega_G=\frac1{|\mathcal H|}\sum_{h=1}^{|\mathcal H|}\omega_h$$3 is approximately $$\omega_G=\frac1{|\mathcal H|}\sum_{h=1}^{|\mathcal H|}\omega_h$$4–$$\omega_G=\frac1{|\mathcal H|}\sum_{h=1}^{|\mathcal H|}\omega_h$$5. The paper gives an explicit practical rule: for target worst-case accuracy $$\omega_G=\frac1{|\mathcal H|}\sum_{h=1}^{|\mathcal H|}\omega_h$$6, evaluate

$$\omega_G=\frac1{|\mathcal H|}\sum_{h=1}^{|\mathcal H|}\omega_h$$7

and sweep $$\omega_G=\frac1{|\mathcal H|}\sum_{h=1}^{|\mathcal H|}\omega_h$$8 on a pilot run.

This version of FedDRM is centered on robustness and fairness rather than privacy-preserving sensing or energy control. Its defining technical contribution is the coupling of a DRO saddle-point objective with a dynamic client-drift penalty and periodic dual-variable updates.

4. Federated aggregation of demand flexibility

In "Federated Aggregation of Demand Flexibility" (Dong et al., 23 Sep 2025), FedDRM denotes a federated framework for learning a shared base set used to aggregate flexibility from demand-side resources. The system model considers a time horizon $$\omega_G=\frac1{|\mathcal H|}\sum_{h=1}^{|\mathcal H|}\omega_h$$9 and $\omega_G$0 DSRs, often exemplified by electric vehicles. For each resource $\omega_G$1, the net charging trajectory is $\omega_G$2, the cumulative energy state satisfies

$\omega_G$3

and the feasible set is the polytope

$\omega_G$4

The true aggregate flexibility is the Minkowski sum

$\omega_G$5

but exact computation is NP-hard. The stated objective is therefore to form a large-volume inner approximation $\omega_G$6 without collecting raw $\omega_G$7.

The construction begins with a shared base set

$\omega_G$8

Each DSR then computes the largest affine image $\omega_G$9. Since

$F(w,\lambda)$00

the aggregate volume is $F(w,\lambda)$01. The paper formulates a bilevel problem that maximizes this aggregate size subject to per-client containment constraints. To render the lower-level tractable, the determinant is replaced by its first-order Taylor proxy $F(w,\lambda)$02, turning each lower-level problem into a convex LP.

The bilevel program is then reformulated as a single-level unconstrained learning task. A projection map $F(w,\lambda)$03 enforces a strictly interior base set, and lower-level optimal-solution functions $F(w,\lambda)$04 and $F(w,\lambda)$05 are substituted into

$F(w,\lambda)$06

where $F(w,\lambda)$07 and $F(w,\lambda)$08. The gradient is decomposed by the chain rule, with $F(w,\lambda)$09 expressed through $F(w,\lambda)$10, and $F(w,\lambda)$11 obtained from a nested-integral representation of the volume. Because all terms of the form $F(w,\lambda)$12 are additive, the framework uses secure summation without revealing individual contributions.

The federated loop broadcasts $F(w,\lambda)$13, lets each DSR compute $F(w,\lambda)$14, solve its LP, and send $F(w,\lambda)$15 and $F(w,\lambda)$16 in anonymized form to the aggregator, which then computes $F(w,\lambda)$17, $F(w,\lambda)$18, their gradients, and the update $F(w,\lambda)$19. The privacy guarantees are explicit: raw user data $F(w,\lambda)$20 never leave the devices, the exchanged objects are low-dimensional compared with raw profiles, and secure summation or encryption can further hide individual contributions.

The numerical study uses $F(w,\lambda)$21 EVs and $F(w,\lambda)$22, with EV parameters sampled uniformly as specified in Table I. The benchmark AVG fixes the base set at $F(w,\lambda)$23. For the normalized per-dimension volume ratio

$F(w,\lambda)$24

the observed median is approximately $F(w,\lambda)$25–$F(w,\lambda)$26, corresponding to a $F(w,\lambda)$27–$F(w,\lambda)$28 per-dimension increase. On downstream tasks, peak-power minimization shows an AVG gap versus perfect of approximately $F(w,\lambda)$29, compared with approximately $F(w,\lambda)$30 for the proposed method, and an improvement over AVG of approximately $F(w,\lambda)$31–$F(w,\lambda)$32. Electricity-cost minimization reports cost savings versus AVG of approximately $F(w,\lambda)$33.

The implementation path is organized into base-set training, real-time aggregation, and disaggregation. During disaggregation, the aggregator computes

$F(w,\lambda)$34

broadcasts $F(w,\lambda)$35, and each DSR sets $F(w,\lambda)$36. Communication uses packets of dimension $F(w,\lambda)$37, and the framework is described as compatible with existing aggregator–DER interfaces such as OpenADR and FERC interconnection.

5. Structural comparison of the three formulations

Although the three methods share a federated topology, they optimize different mathematical objects and serve different operational ends (Rezazadeh et al., 2022, Krouka et al., 21 May 2025, Dong et al., 23 Sep 2025). In the smart micro-grid formulation, the global object is an averaged model $F(w,\lambda)$38 for local SAC agents. In DRDM, the global state comprises both a model variable $F(w,\lambda)$39 and a dual variable $F(w,\lambda)$40, and the server alternates primal aggregation with projected dual ascent. In demand-flexibility aggregation, the server does not aggregate a predictive model; instead it updates the shared polyhedral parameter $F(w,\lambda)$41 by combining client-computed sensitivities.

The data exchange pattern also differs. The smart micro-grid version explicitly exchanges only neural-network parameters and keeps raw consumption and generation measurements local. The flexibility-aggregation version explicitly keeps raw $F(w,\lambda)$42 local and exchanges $F(w,\lambda)$43 through anonymized or secure summation. The DRDM formulation, by contrast, is primarily described through robustness, fairness, local-step scheduling, and communication-energy trade-offs; its key server-client messages are global model broadcasts, local model returns, snapshot iterates, and sampled losses.

The performance criteria are similarly non-overlapping. The micro-grid work evaluates cumulative reward, CO$F(w,\lambda)$44 emissions, convergence speed, and battery charge cycles. DRDM evaluates average and worst-case client accuracies, inter-client standard deviation, and total client energy. The flexibility-aggregation framework evaluates inner-approximation volume, peak-power minimization gap, and electricity-cost minimization. A direct empirical comparison across the three is therefore not meaningful without changing problem definitions.

A plausible implication is that “FedDRM” functions more as a naming convention attached to a federated decomposition pattern than as a stable algorithmic family. In one case the local problem is an MDP with entropy-regularized control, in another it is a convex-smooth DRO saddle-point problem, and in the third it is a polyhedral geometric learning problem derived from a bilevel program.

6. Extensions, implementation directions, and interpretive caveats

The smart micro-grid paper lists three explicit extensions: multi-layer federations for regional micro-grids, peer-to-peer energy trading via multi-agent RL, and differential privacy on updates to further protect prosumer data (Rezazadeh et al., 2022). These directions remain within the same energy-management setting but would alter the communication topology, local interaction structure, or privacy mechanism.

The DRDM formulation emphasizes implementation choices for local steps rather than architectural extension. Its practical guideline selects $F(w,\lambda)$45 by combining target worst-case accuracy with communication conditions such as SNR and bandwidth, then minimizing the modeled total energy cost over a small grid of candidate values (Krouka et al., 21 May 2025). This gives the method a hardware- and channel-aware operational interpretation that is absent from the other two versions.

The demand-flexibility framework is oriented toward deployment in grid operation. Its prescribed workflow separates historical or freshly sampled training from real-time aggregation and disaggregation, and it states that solver routines can run on aggregator servers or lightweight edge devices, with integration into existing interfaces such as OpenADR and FERC interconnection (Dong et al., 23 Sep 2025). In that formulation, the federated component is not incidental to learning accuracy; it is the mechanism that makes collaborative base-set optimization possible without exchanging raw user trajectories.

A common misconception is that FedDRM names a unique algorithm with a transferable implementation recipe. The literature summarized here does not support that reading. The shared term covers distinct objectives, update rules, and guarantees. The technically precise use of the name therefore depends on immediate qualification by domain: federated DRL for smart micro-grids, distributionally robust federated learning with client-drift minimization, or federated aggregation of demand flexibility.